Techniques for semantic searching

ABSTRACT

Techniques are disclosed for querying, retrieval, and presentation of data. A data analytic system can enable a user to provide input, through a device to query data. The data analytic system can identify the semantic meaning of the input and perform a query based on the semantic meaning. The data analytic system can crawl multiple different sources to determine a logical mapping of data for the index. The index may include one or more subject areas, terms defining those subject areas, and attributes for those terms. The index may enable the data analytic system to perform techniques for matching terms in the query to determine a semantic meaning of the query. The data analytic system can determine a visual representation best suited for displaying results of a query determined by semantic analysis of an input string by a user.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present application claims priority and benefit as follows:

-   -   1) The present application claims priority and benefit as a         Continuation-in-Part Non-Provisional application of U.S.         Non-Provisional application Ser. No. 14/861,893, filed Sep. 22,         2015 and entitled “SEMANTIC SEARCHES IN A BUSINESS INTELLIGENCE         SYSTEM,” which claims priority and benefit from U.S. Provisional         Application No. 62/055,402, filed Sep. 25, 2014, entitled         “SEMANTIC SEARCHES IN A BUSINESS INTELLIGENCE SYSTEM”;     -   2) The present application claims priority and benefit of U.S.         Provisional Application No. 62/356,471, filed Jun. 29, 2016 and         entitled “TECHNIQUES FOR SEMANTIC SEARCHING”; and     -   3) The present application claims priority and benefit of U.S.         Provisional Application No. 62/396,088, filed Sep. 16, 2016 and         entitled “TECHNIQUES FOR SEMANTIC SEARCHING.”

This application is related to U.S. Non-Provisional application Ser. No. 15/273,567, filed Sep. 22, 2016, titled “DAY-BY-DAY,” which claims the benefit and priority from U.S. Provisional Application No. 62/246,031 filed Oct. 24, 2015.

The entire contents of each of the above-identified patent applications are incorporated herein by reference for all purposes and are considered part of this disclosure.

BACKGROUND

Large computer systems, such as enterprise and cloud computer systems use large, very complex data stores in the form of databases and data warehouses, as examples. These stores are accessible by sophisticated trained users that can produce the requested information by using complex query structures and reports in the vernacular of the databases.

Not all users have sophistication and training to search and access data. These users area comfortable with ‘textual searching’ or user-friendly interfaces provided by search engines. Large-scale systems do not have the capability to provide the user with this type of access, nor do they produce the responses in a format that the user can understand. Users may not be familiar with terminology to determine terms for a query and an order in which to present those terms. As a result, users may be unable to locate information they seek unless they know the proper terminology for the source of the data. Computer systems implemented to perform a search may perform endless processing to locate data for a query. The data may not be responsive to a user's query.

Further, with the advent of mobile devices, such as smart phones and tablets, users have become accustomed to accessing data and getting answers wherever they are located. They often demand to input textual questions, even into complex, large, databases and data warehouses, and to receive the answers in an easily understandable format. Mobile devices further introduce new challenges for presenting users with choices and results for querying data.

BRIEF SUMMARY

In certain embodiments, techniques are provided (e.g., methods, systems, and computer-readable media) for querying, retrieval, and presentation of data. A data analytic system is disclosed that enables a user to provide input, through a device (e.g., a mobile device), to query data. The input may be provided without a syntax of a known language (e.g., English). In some embodiments, the input may include terms defining the query, without a syntax such as articles or other terms that define the terms of the query. The data analytic system can identify the semantic meaning of the input and perform a query based on the identified semantic meaning. Based on the identified semantic meaning, the data analytic system can query one or more data sources to retrieve data responsive to the query. One or more data sources may be searched for responsive data.

The data analytic system may generate an index of data accessible from one or more data sources. The data analytic system can crawl multiple different sources to determine a logical mapping of data for the index. The index may include one or more subject areas, terms defining those subject areas, and attributes for those terms. The index may enable the data analytic system to perform techniques for matching terms in the query to determine a semantic meaning of the query.

In some embodiments, the data analytic system can determine a visual representation best suited for displaying results of a query determined by semantic analysis of an input string by a user. A graphical interface is generated to display the results according to the best-suited visual representation. The visual representation may be chosen based on a user's past behavior in interaction with the data analytic system. The visual representation may be selected based on the number of terms for which data is displayed and the attributes requested to display information about the terms.

In some embodiments, a computer system may implement techniques disclosed herein. The computer system may include a memory coupled to one or more processors, the memory storing instructions, where the instructions, upon execution by the one or more processors, cause the one or more processors to perform one or more methods or operations disclosed herein. In at least one embodiment, the one or more processors and the memory are included in a mobile device. Yet other embodiments relate to systems, consumer devices, computer program products, systems, and machine-readable storage media, which can employ or store instructions to cause methods and operations disclosed herein to be performed.

In some embodiments, a method may include processing an input string as a search query for data from one or more data sources, where the input string is processed to identify a plurality of terms. The method may include performing a first comparison of each of the plurality of terms to a plurality of subject areas in a subject area index. The method may include, based on the comparing, selecting a set of subject areas in the plurality of subject areas, the set of subject areas selected as having a closest match with the plurality of terms. The method may include performing a second comparison of the plurality of terms to a subject area column index based on the selected set of subject areas, the second comparison includes comparing each of the plurality of terms to terms in each of the selected set of subject areas of the subject area column index and to one or more attributes of each of the terms in each of the selected set of subject areas. The method may include, based on the second comparison, selecting one or more terms of the plurality of terms based on a threshold number of terms that match from the plurality of terms. The method may include comparing each of the selected one or more terms to a value for each of the plurality of terms in the selected set of subject areas of the subject area column index. The method may include determining one or more matching terms of the selected one or more terms based on the comparing of the selected one or more terms to the value for each of the plurality of terms in the selected set of subject areas. The method may include generating a query based on the determined one or more matching terms.

In at least one embodiment, processing the input string to identify the plurality of terms includes: tokenizing the input string to identify a set of terms based one or more characters; determining a stem word for each of the set of terms; determining a relationship between any pair of terms in the set of terms; and generating the plurality of terms based on the set of terms and the relationship between any terms in the set of terms.

In some embodiments, the method may include generating one or more first data structures as the subject area column index based on selected the set of subject areas. The subject area column index may have a different plurality of columns for each of the selected set of subject areas. Each of the plurality of columns may be associated with one or more attributes of each of the terms in a subject area. In some embodiments, the method may include generating one or more second data structures having one or more values for each of the one or more attributes for a subject area corresponding to the column in the plurality of columns, where the comparing is performed using the one or more second data structures.

In some embodiments, the one or more matching terms includes a set of terms. Generating the query may include determining an order in the set of terms based on the comparing of the selected one or more terms to the value for each of the plurality of terms in the selected set of subject areas.

In some embodiments, the method may include performing a crawl process based on a crawl configuration. The method may include generating one or more data structures as the subject area index based on the crawl process.

In at least one embodiment, the method includes retrieving the data from one or more data sources based on the query. The query may be different from the search query. The query may have a format of a logical structured query language (SQL). In some embodiments, the method may include identifying one or more dimensions for the one or more matching terms. The method may include using the one or more dimensions, determining a layout for presenting the retrieved data. The method may include generating a graphical interface based on the layout.

The techniques described above and below may be implemented in a number of ways and in a number of contexts. Several example implementations and contexts are provided with reference to the following figures, as described below in more detail. However, the following implementations and contexts are but a few of many.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a high-level diagram of an architecture for a data analytic system in accordance with an embodiment.

FIG. 2 illustrates a detailed diagram of a data analytic system in accordance with an embodiment.

FIG. 3 illustrates a detailed diagram of a data analytic system in accordance with an embodiment.

FIG. 4 illustrates a flow diagram of a process for generating a data index in accordance with an embodiment.

FIG. 5 illustrates a detailed diagram of a query system in accordance with an embodiment.

FIG. 6 illustrates data structures of a data index according to some embodiments.

FIG. 7 illustrates a reference data structure according to some embodiments.

FIG. 8 illustrates a process for determining a semantic meaning of an input string according to some embodiments.

FIG. 9 illustrates a diagram of a data structure for determining a visualization of data according to some embodiments.

FIGS. 10-17 illustrate graphical interfaces for searching and viewing search results according to an embodiment.

FIG. 18 depicts a simplified diagram of a distributed system for implementing an embodiment.

FIG. 19 illustrates a simplified block diagram of one or more components of a system environment in which services may be offered as cloud services, in accordance with an embodiment of the present disclosure.

FIG. 20 illustrates an exemplary computer system that may be used to implement an embodiment of the present disclosure.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. However, it will be apparent that various embodiments may be practiced without these specific details. The figures and description are not intended to be restrictive. The computing environment disclosed in the figures is not intended to suggest any limitation as to scope of use or functionality of described embodiments.

In certain embodiments, techniques are provided (e.g., methods and systems) for querying, retrieval, and presentation of data. A data analytic system is disclosed that enables a user to provide input, through a device (e.g., a mobile device), to query data. The input may be provided without a syntax of a known language (e.g., English). In some embodiments, the input may include terms defining the query, without a syntax such as articles or other terms that define the terms of the query. The data analytic system can identify the semantic meaning of the input and perform a query based on the identified semantic meaning. Based on the identified semantic meaning, the data analytic system can query one or more data sources to retrieve data responsive to the query. One or more data sources may be searched for responsive data.

The data analytic system may generate an index of data accessible from one or more data sources. The data analytic system can crawl multiple different sources to determine a logical mapping of data for the index. The index may include one or more subject areas, terms defining those subject areas, and attributes for those terms. The index may enable the data analytic system to perform techniques for matching terms in the query to determine a semantic meaning of the query.

In some embodiments, the data analytic system can determine a visual representation best suited for displaying results of a query determined by semantic analysis of an input string by a user. A graphical interface is generated to display the results according to the best-suited visual representation. The visual representation may be chosen based on a user's past behavior in interaction with the data analytic system. The visual representation may be selected based on the number of terms for which data is displayed and the attributes requested to display information about the terms.

I. High-Level Overview of Data Analytic System

FIG. 1 illustrates an example of a system 100 including a data analytic system (referred to as an “Intelligence Ask System”) in accordance with some embodiments. One or more of the disclosed techniques may be implemented in or involve one or more computer systems.

System 100 may include one or more client systems 102 (referred to herein as “client system” or “client”) and data analytic system 150. One example of a client system is a mobile device (e.g., a smartphone) as illustrated in FIG. 2. Client systems may be operated by one or more users. Data analytic system 150 may be implemented by a computer system. Data analytic system 150 may perform operations according to techniques disclosed herein. Specifically, data analytic system 150 may perform operations for querying, retrieving, and/or displaying data accessible from one or more data sources 110. Operations may be performed in response to input received from client system 102. Data analytic system may perform operations to provide services to one or more users through client system 102.

System 100 may include an enterprise computer system 160. Enterprise computer system 160 may provide client systems with access to objects, such as applications (e.g., applications, services, or data. Access to objects may be provided via network 130. In the example shown in FIG. 1, client system 102 may access objects, such as applications and data objects.

Client system 102 may provide an application 104 to access features (which may be provided as services) of data analytic system 150. Client system 102 may access services of data analytic system 150 using application 104. Data analytic system 150 may enable a user to query data from one or more data sources 110. Application 104 may provide one or more graphical interfaces, e.g., graphical interface 106, for receiving input to query data and for displaying the results of the query. In some embodiments, application 104 may enable a user to configure operation of data analytic system 150, such as a crawl configuration for indexing data from data sources 110.

A query processed by data analytic system 150 may be limited to the data accessible to the user providing the input. For example, in an enterprise system, data may be restricted based on a role of the user. In some embodiments, a query may be performed against the data the user requesting the query is permitted to access.

Client system 102, data analytic system 150, data sources 110, and enterprise computer system 150 may be communicatively connected via one or more communication networks 130. Examples of communication networks include, without restriction, the Internet, a wide area network (WAN), a local arear network (LAN), an Ethernet network, a public or private network, a wired network, a wireless network, and the like, and combinations thereof. Different communication protocols may be used to facilitate the communications including both wired and wireless protocols such as IEEE 802.XX suite of protocols, TCP/IP, IPX, SAN, AppleTalk, Bluetooth®, and other protocols. In general, communication network 130 may include any communication network or infrastructure that facilitates communications between client systems and computer systems, such as enterprise computer system and data analytic system 150.

Data analytic system 150 may be implemented using a computer system, which may comprise one or more computers and/or servers which may be general purpose computers, specialized server computers (including, by way of example, PC servers, UNIX servers, mid-range servers, mainframe computers, rack-mounted servers, etc.), server farms, server clusters, distributed servers, or any other appropriate arrangement and/or combination thereof. For example, data analytic system 150 may correspond to a computer system for performing processing as described herein according to an embodiment of the present disclosure. The computing system that makes up data analytic system 150 may run any number of operating systems or a variety of additional server applications and/or mid-tier applications, including HTTP servers, FTP servers, CGI servers, Java servers, database servers, and the like. Exemplary database servers include without limitation those commercially available from Microsoft, and the like. In one example, data analytic system 150 may be included in or implemented as a service of Business Intelligence provided by Oracle® corporation. In various embodiments, data analytic system 150 may be configured to run one or more services or software applications described in the foregoing disclosure.

In some embodiments, data analytic system 150 may be implemented as an enterprise computing system or a cloud computing system comprising one or more computers and/or servers that may include those described above. For example, data analytic system 150 may be implemented as part of enterprise computer system 160. Data analytic system 150 may include several subsystems and/or modules, including some, which may not be shown. For example, data analytic system 150 may include crawl subsystem 152 and query subsystem 180. Crawl subsystem 152 may include scheduler 154 and crawl manager 156. Query subsystem 180 may include input handler 182, index manager 184, query generator 186, an visualization manager 188. Each of the elements of data analytic system 150 is described in detail further below. Data analytic system 150 may have more or fewer subsystems and/or modules than shown in the figure, may combine two or more subsystems and/or modules, or may have a different configuration or arrangement of subsystems and/or modules. Subsystems and modules of data analytic system 150 may be implemented in software (e.g., program code, instructions executable by a processor), firmware, hardware, or combinations thereof. In some embodiments, the software may be stored in a memory (e.g., a non-transitory computer-readable medium), on a memory device, or some other physical memory and may be executed by one or more processing units (e.g., one or more processors, one or more processor cores, one or more GPUs, etc.).

In certain embodiments, data analytic system 150 may also provide other services or software applications can include non-virtual and virtual environments. In some embodiments, these services may be offered as web-based or cloud services or under Software as a Service (SaaS) model to the users of client systems. The services offered by data analytic system 150 may include application services. Application services may be provided by data analytic system 150 via a SaaS platform. The SaaS platform may be configured to provide services that fall under the SaaS category. The SaaS platform may manage and control the underlying software and infrastructure for providing the SaaS services. By utilizing the services provided by the SaaS platform, customers can utilize applications executing in data analytic system 150, which may be implemented as a cloud infrastructure system. Users can acquire the application services without the need for customers to purchase separate licenses and support. Various different SaaS services may be provided. Users operating client systems 102, 120 may in turn utilize one or more applications to interact with data analytic system 150 to utilize the services provided by subsystems and/or modules of data analytic system 150.

Data analytic system 150 may include at least one memory, one or more processing units (or processor(s)), and storage. The processing unit(s) may be implemented as appropriate in hardware, computer-executable instructions, firmware, or combinations thereof. Computer-executable instructions or firmware implementations of the processing unit(s) may include computer-executable or machine-executable instructions written in any suitable programming language to perform the various operations, functions, methods, and/or processes described herein. The memory in data analytic system 150 may store program instructions that are loadable and executable on the processing unit(s), as well as data generated during the execution of these programs. The memory may be volatile (such as random access memory (RAM)) and/or non-volatile (such as read-only memory (ROM), flash memory, etc.). The memory may be implemented using any type of persistent storage device, such as computer-readable storage media. In some embodiments, computer-readable storage media may be configured to protect a computer from an electronic communication containing malicious code. The computer-readable storage media may include instructions stored thereon, that when executed on a processor, perform the operations described herein.

Data analytic system 150 may also include or be coupled to storage, which may be implemented using any type of persistent storage device, such as a memory storage device or other non-transitory computer-readable storage medium. In some embodiments, local storage may include or implement one or more databases (e.g., a document database, a relational database, or other type of database), one or more file stores, one or more file systems, or combinations thereof. For example, data analytic system 150 may be coupled to or may include one or more data stores, such as reference store 140, data index 142, and semantic model 146. The memory and the additional storage are all examples of computer-readable storage media. For example, computer-readable storage media may include volatile or non-volatile, removable or non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. Although depicted as different data stores, more or fewer data stores may be implemented to store data according to the techniques disclosed herein.

System 100 may also include or be coupled to storage, such as data sources 110, which may be implemented using any type of persistent storage device, such as a memory storage device or other non-transitory computer-readable storage medium. In some embodiments, local storage may include or implement one or more databases (e.g., a document database, a relational database, or other type of database), one or more file stores, one or more file systems, or combinations thereof. The memory and the additional storage are all examples of computer-readable storage media. For example, computer-readable storage media may include volatile or non-volatile, removable or non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. As an example, data sources 110 may include, without limitation, a data warehouse 112, a data mart 114, an on-line analytical process (OLAP) cube 116, a cloud data source 118, spreadsheet data 120, and user-defined data 122.

Data in any one of the data sources 110 may include data received from an enterprise computer system, data sent to an enterprise computer system, data processed by an enterprise computer system, or combinations thereof. Data may include enterprise data, business data, consumer data, or any other type of data. In certain embodiments, data may include data processed, stored, used, or communicated by an application or a service executing in an enterprise computer system. For example, enterprise data may include business data (e.g., business objects) such as JSON (JavaScript Object Notation) formatted data from enterprise applications, structured data (e.g., key value pairs), unstructured data (e.g., internal data processed or used by an application, data in JSON format, social posts, conversation streams, activity feeds, etc.), binary large objects (BLOBs), documents, data using representational state transfer (REST) techniques (referred to herein as “RESTful data”), system data, configuration data, synchronization data, or combinations thereof.

In an enterprise computing environment, client systems may communicate with data analytic system 150 to query data and obtain results, which can be viewed at the client systems. Data analytic system 150 may receive queries from client systems. Data analytic system 150 may be configured based on a configuration specified to data analytic system. For example, data analytic system 150 may be configured using techniques disclosed in “Configuration” of Appendix A in U.S. Provisional Application No. 62/055,402, filed Sep. 25, 2014 and entitled “SEMANTIC SEARCHES IN A BUSINESS INTELLIGENCE SYSTEM.” Such techniques may enable configuration of a query (e.g., a crawl) as properties defined by one or more criteria in a configuration file.

Crawl subsystem 152 may perform crawl operations to generate data index 142. Data analytic system 150 may create a logical mapping of data in data index. The logical mapping may be stored in semantic data model 146. The semantic data model may be used to determine a mapping of data index 142. The data index 142 may be used for semantic analysis of a query. By generating a data index, a search system can be implemented with reduced storage, such that hundreds of gigabytes of storage can be reduced to a fraction of the storage. By reducing the storage, processing of a computer system implementing a search can be increased as less data will be searched to identify a similarity of terms for semantic analysis. The data index further enables a search system to combine different subject areas or domains of data into a single index, which may be further limited by the access permitted to the group of users. By reducing the storage for data and creating an index of data based on many data sources, multiple data sources may be searched for semantic analysis of text-based query.

Query subsystem 180 to process an input string by a user to determine a semantic meaning of the input string. Data analytic system 150 may process the input string into words and compare those words to data index 142 to determine a closest match for the terms. A semantic meaning of the terms may be determined based on a similarity measure with the terms. Techniques for semantic analysis are described further below. Query subsystem 180 may determine a best-suited visual representation for the results of a query based on the query that is requested.

Enterprise computer system 160 may provide services such as access to applications 162. Enterprise computer system 160 may include an access management system 164, such Oracle Access Manager provided by Oracle® corporation. Access management system 164 may be used to determine a level of access of a user operating client system 102. Access may be defined by one or more levels. One or more roles may be defined for access, which are associated with one or more levels. Enterprise computer system may include web server 166, which handles communications (e.g., requests and responses) internally and externally to enterprise computer system 160. Data analytic system 150 may request access management system 164 to provide access information to enable data analytic system 150 to determine a

Enterprise computer system 160 may include presentation server 168. Presentation server computer 168 may provide a presentation service, such as Business Intelligence Presentation Service (BIPS) provided by Oracle® corporation. Data analytic system 150 may communicate results of a query to presentation server 168. The results may be communicated with a best-suited visual representation for the results. Presentation server 168 can generate a report for display in a graphical interface (e.g., graphical interface 106). The report may indicate the results of the query according to the visual representation. Described below with references to FIGS. 10-17 are examples of techniques for displaying results.

In some embodiments, data analytic system 150 may maintain a presentation mapping in semantic model 146. The presentation mapping may indicate a hierarchy for displaying data. Examples of the presentation mapping are described further with reference to FIG. 9.

Enterprise computer system 160 may include intelligence server computer 170. Intelligence server 170 may provide an intelligence service, such as Oracle Business Intelligence service provided by Oracle® corporation. Data analytic system 150 may communicate with intelligence server 170 to obtain data from one or more data sources. Intelligence server 170 may aggregate data from one or more data sources 110 and a create a physical mapping of data to data sources. Data analytic system 150 can request data from intelligence server 170 for crawling to generate data index 142. In some embodiments, data analytic system 150 may communicate with intelligence server 170 via presentation server 168.

In some embodiments, enterprise computer system 160 may have an interface (e.g., an application programming interface) enabling communication. Enterprise computer system 160, in particular presentation server 168 and intelligence server 170 may support communication using a query language. The query language may be structured, unstructured, or semi-structured. For example, data analytic system 150 may communicate with enterprise computer system 150 using a logical query language. During crawling, data analytic system 150 may request data to build data index 140 by issuing one or more logical queries, each defining a the subject matter for which data is requested. During searching, data analytic system 150 may request data for a query based on a logical query constructed after determining a semantic meaning of a query. Terms defining in data index 142 may be used to identify the data for the subject matter requested.

Through this disclosure, various flowcharts and techniques are disclosed illustrating processes according to some embodiments of the present disclosure. Individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations may be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed, but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination may correspond to a return of the function to the calling function or the main function.

The processes depicted in the figures may be implemented in software (e.g., code, instructions, program) executed by one or more processing units (e.g., processors cores), hardware, or combinations thereof. For example, data analytic system 150 of FIG. 1 can implement the processes described with reference to any of the figures. Any of the processes may be implemented as a service. In some embodiments, any of the elements in the figures may be implemented with more or fewer subsystems and/or modules than shown in the figure, may combine two or more subsystems and/or modules, or may have a different configuration or arrangement of subsystems and/or modules. Subsystems and modules may be implemented in software (e.g., program code, instructions executable by a processor), firmware, hardware, or combinations thereof. In some embodiments, the software may be stored in a memory (e.g., a non-transitory computer-readable medium), on a memory device, or some other physical memory and may be executed by one or more processing units (e.g., one or more processors, one or more processor cores, one or more GPUs, etc.).

The particular series of processing steps in the figures is not intended to be limiting. Other sequences of steps may also be performed according to alternative embodiments. For example, alternative embodiments of the present disclosure may perform the steps outlined above in a different order. Moreover, the individual steps illustrated in the figures may include multiple sub-steps that may be performed in various sequences as appropriate to the individual step. Furthermore, additional steps may be added or removed depending on the particular applications. One of ordinary skill in the art would recognize many variations, modifications, and alternatives.

In some embodiments, data may be stored using one or more data structures. Data structures may be organized in a variety of ways depending on how, what, and/or where data is stored. Although each of the data structures are shown including particular data, more or fewer data structures may be implemented to store the data. A data structure can include a reference to other data structures. The data structures may be implemented using one or more types of data structures including, without restriction, a linked list, an array, a hashtable, a map, a record, a graph, or other type of data structure. A data structure may be implemented in a hierarchical manner. Each of the data structures may be defined in a declarative manner based on input by a user. The data structures can be defined based on a template, e.g., a template defined based on a markup language, such as Extended Markup Language (XML). A data structure may have one or more formats, also referred to as a document format.

II. Detailed Views of Data Analytic System

FIG. 2 illustrates a detailed diagram of a system 200 in accordance with an embodiment. System 200 may include several subsystems and/or modules of data analytic system 150 and/or enterprise computer system 160 of FIG. 1. Client system 12 (e.g., a mobile device) may be a client system that communicates with data analytic system 150. Data analytic system 150 may provide client system 12 with a search query service 14. Search query service 14 may enable a user operating client system 12 to query data in one or more data sources. Search query service 14 may be provided through an application on client system 12. For example, client system 12 may receive input in the form of a query from a user. Client system 12 may request search query service 14 to perform a search based on the query.

In one embodiment, search query service 12 may be provided as a Java® service running in a J2EE environment of data analytic system 150. A user can provide input to client system 12, which is provided to search query service 14. Search query service 14 can process the input to generate one or more queries for data accessible through enterprise computer system 160. The generated queries can be submitted to enterprise computer system 160 to retrieve the data for the user. Search query service 14 may be exposed by data analytic system 150. In particular, search query service 14 may provide an interface for functionality to communicate with BIPS 16, scheduler 17, and search index 19.

In some embodiments, the search query service 14 may generate data in a format based on results obtained from a query. The data may be in a tabular format. Search query service 14 may send a query to BIPS 16, which exposes access to the BI Server 18. BIPS 16 may perform a query on data stored by BI Server 16. BIPS 16 may provide results back to search query service 14. BIPS 16 may also provide search query service with metadata report and interfaces (e.g., dashboards) for presentation of results for a search. Search query service 14 may perform operations disclosed herein as being performed by operations disclosed herein. For example, search query service 14 may perform the algorithms for generating an index for searching. The index may be stored in search index 19.

In some embodiments, data analytic system 150 may include scheduler 17 (e.g., scheduler 154 of FIG. 1). Scheduler 17 may schedule and manage queries performed for the search query service 14. Scheduler 17 may manage operation of data analytic system 150 according to one or more criteria, such as time, date, one or more events, system-defined attributes, user-defined attributes, or a combination thereof. For example, scheduler 17 may be configured to operate based on user input. Input may be provided to configure crawling of data sources to generate a search index 19 of data. Search index 19 may include one or more terms defining a corpus of data.

System 200 may include a presentation server 16, such as presentation server 168, for providing one or more graphical interfaces for display at client system 14. Presentation server 16 (also referred to as a “presentation layer”) may be provided as part of a service, such as a Business Intelligence Presentation Service (BIPS), provided by Oracle® corporation. Input may be received through an interface presented at client system 12. Presentation server 16 may request data analytic system 150 to operate based on input presentation server 16 receives.

Scheduler 17 may operate to initiate operation of crawl manager 154 of FIG. 1 to crawl one or more data sources to build a search index of data 19 to be used by search query service 14 to generate queries based on user input. Search index 19 may be generated by a crawl process, such as the process described with reference to FIGS. 3 and 4. Scheduler 17 may operate based on a schedule indicated by input received from search query service 14. Crawling may be performed based on a crawl configuration. Scheduler 17 may schedule and manage data indexing based on input received from a user. Scheduler 17 may initiate a crawl with to build search index 19.

In some embodiments, a crawl may be initiated by an element other than search query service 14. For example, scheduler 17 of data analytic system 150 might be operated based on input through an interface provided by data analytic system 150. Through the interface, a user (e.g., an administrator) can configure a crawl search process to generate search index 19 based on a schedule independent of user input.

The crawl may include communicating with an intelligence server 18 (“BI Server”), such as intelligence server 170. Search query service 14 may initiate a crawl based on user input. Search query service 14 may initiate a search crawl by communication with BIPS 16 that provides an interface to query BI Server 18 for a search or crawl. A crawl process may include sending queries to intelligence server 18 to using a query language, such as a structured query language (SQL). The query language may be a logical query language to indicate the data to be queried. As described below, intelligence server 18 can maintain a physical mapping of data from one or more data sources. BI server 18 can process a logical query from data analytic system 150 and retrieve data from one or more data sources. In some embodiments, a query for a crawl may be directed to all data accessible for all levels of access so that the crawl is performed to capture all data. The crawl may be performed with a level of access that permits access to all data so that search index is based on all data accessible from data sources. BI Server 18 may initiate a crawl on data sources based on the query received from data analytic system 150. Multiple queries may be submitted to BI server 18 such that BI server 18 performs crawls concurrently.

Once search index 19 has been created, search query service 14 can facilitate a search based on input received from client system 12. Presentation server 16 may provide an interface to receive a search query from search query service 14 based on user input. Search query service 14 can allow a user that does not have a background in a specific language of a data source to perform a textual search of the data to get an answer to a simple query that is not defined by a structured language. In some embodiments, search query service 14 may provide a graphical interface to receive input as a query. For example, a user can provide input such as a ‘regular’ question. Examples include queries like, “What is the revenue of iPods for the North East in 2011?” or “Compare 2011 Q1 revenue to 2012 Q3.” An input string of a query may be processed as disclosed herein to determine one or more terms, such as each word in a query. The terms may be used by a process, such as process 800 of FIG. 8 to generate a query. Search query service 14 may implement process 800 of FIG. 8 to generate a query in a format understandable by presentation server 16. In some embodiments, search query service 14 may indicate a presentation type for displaying the data for the query provided to presentation server 16.

Data analytic system 150 can process the query to search for a result using the search index 19. The search may be performed using techniques disclosed herein, such as the processes described with reference to FIGS. 5 and 8. The data identified as a result of the search may be processed by search query service 14 into a query defined based on the user's input. The query may be provided to presentation server 16, which then communicates with BI server 18 to retrieve the data responsive to the query. Presentation server 16 provides the data in a graphical interface, allowing the user to visualize the answer to the user's input, received in a form of a text-based search. The data may be presented in a graphical visualization to enables the user to understand the data. When a client system is a small device, understanding the data becomes difficult. A graphical visualization of the data enables the user to view the data without limitations of a client system having limited display capabilities.

III. Detailed Views of Crawl Subsystem and Search Subsystem

FIG. 3 illustrates a detailed diagram of a data analytic system 300 in accordance with an embodiment. All or a portion of data analytic system 150 may be included in data analytic system 300. In some embodiments, data analytic system 300 may be implemented as an application executing in data analytic system 150. An example of an application is a business intelligence application (“BI application”) provided by Oracle® corporation.

Data analytic system 300 may include crawl subsystem 152 of FIG. 1 and query subsystem 180. Crawl subsystem 152 may provide a crawl service 20. Query subsystem 180 may provide a search service 30. Search service 30 may perform operations described with reference to search query service 14 of FIG. 2. Crawl service 20 and query service 30 may be Java® services running in a Java® platform enterprise environment (e.g., J2EE). The crawl and the query service may be initiated using graphical interfaces, such as those shown in U.S. patent application Ser. No. 14/861,893, filed Sep. 22, 2015 and entitled “SEMANTIC SEARCHES IN A BUSINESS INTELLIGENCE SYSTEM” and U.S. Provisional Application No. 62/055,402, filed Sep. 25, 2014 and entitled “SEMANTIC SEARCHES IN A BUSINESS INTELLIGENCE SYSTEM.”

Crawl subsystem 152 may provide crawl service 20 according to a schedule. The schedule may be managed by scheduler 154. The crawl service 22 may invoke a crawl manager 22 included in crawl subsystem 152. Crawl manager 22 may be crawl manager 156 of FIG. 1. Crawl manager 22 can manage the one or more crawls to query data from one or more data sources. Crawl manager 22 may query data to generate an index 29 of the data obtained from one or more data sources. Index 29 may be part of a logical data mapping of a semantic data model. As illustrated and described using techniques and interfaces disclosed in U.S. patent application Ser. No. 14/861,893, filed Sep. 22, 2015 and entitled “SEMANTIC SEARCHES IN A BUSINESS INTELLIGENCE SYSTEM” and U.S. Provisional Application No. 62/055,402, filed Sep. 25, 2014 and entitled “SEMANTIC SEARCHES IN A BUSINESS INTELLIGENCE SYSTEM,” a crawl subsystem may be managed and controlled by one or more graphical interfaces. Crawl manager 22 may operate based on input from a user defining one or more crawls. The input may define a schedule for initiating the crawls. A crawl may be defined using a query language, which is structured, unstructured, or semi-structured. A crawl may be defined using any language, such as plain English. In some embodiments, a crawl may be defined as part of automated process to index data from one or more data sources. Crawls may be defined a configuration (e.g. crawl configuration). Embodiments for generating an index of data are described with reference to FIG. 4.

Crawl manager 22 may initiate one or more crawls, each executed as a crawl task, such as crawl task 24. Multiple crawl tasks may be initiated, some of which may run concurrently (i.e., at the same time). The crawl manager 22 may, for example, limit the number of crawls operating simultaneously to one, and set up a queue for the other crawl requests. A crawl task 24 may be initiated by scheduler 154, an automated process that performs a crawl. The progress of a crawl, such as the statistics of number of areas indexed, number of subject area columns indexed, total number data values indexed, etc., may be stored in log files as informational entries. The areas indexed may include metadata, such as measures, dimensions and data members and attributes. Once a crawl has been completed, a user may be notified (e.g., through an interface) using the delivery of a message, success or failure. Further, crawl manager 22 may provide monitoring of the crawl. A user may receive an email indicating that the crawl has begun, with the option of a link to allow the user to stop the crawl. When the crawl completes, another email may notify the user of its completion.

Upon execution, a crawl task may be executed to perform one or more queries of data from one or more data sources. Queries may be executed based on the language or a query defined for the crawl. In some embodiments, crawl subsystem 152 may include a query executor 26. A query executor 26 may convert a query for a crawl task into a language for querying one or more data sources to generate index 29. Index 29 may include a corpus of terms grouped by one or more subject areas. The index 29 may be used by query subsystem 180 to generate a query defined based on the user's input for a search. For example, a query for a crawl task may be converted into a query defined by a logical query language, such as a logical structured query language (logical SQL).

Queries generated by query executor 26 may be executed against one or more data sources. In some embodiments, queries may be issued to a presentation server (e.g., presentation server 168) that provides a presentation service 38, such as BIPS. BIPS 38 may request an intelligence server 40, such as a business intelligence server (BI Server), to execute the query. Intelligence server 40 may be intelligence server 170. BI Server 40 may use a physical data mapping of data sources to execute the query to identify data responsive to the query from one or more data sources. In some embodiments, query executor 26 may send queries directly to a data source. Crawl subsystem 152 may utilize an index writer 28 to index queries and the data responsive to the queries in index 29.

Query subsystem 180 may provide a search service 30 (e.g., search query service 14). Search service 30 may enable a user to search data as an unstructured query. For example, query subsystem 180 allows a user to input ‘textual searches’ in the form most users are familiar. When a user enters a desired search, a search process converts it to queries and accesses the index of the semantic layer to provide the answers. The search process is further described with references to FIGS. 5 and 8.

In at least one embodiment, query subsystem 180 may receive a request to search. The request may be received via an interface (e.g., a search interface) provided for search service 30. When the users launches the search interface on the user's device, typically a smart phone or other mobile device, that starts the search service 30. The user then inputs a search term or terms, which can be of varying levels of complexity.

Upon receiving a query for search service 30, search query rewriter 32 may perform processing to adjust and/or modify the query for searching index 29. For example, an input defining a query may be processed according to techniques described with reference to FIGS. 5 and 8 to produce a set of terms (e.g., an array of terms) to search index 29. Index searcher 34 of query subsystem 180 may perform a search on index 29 based on the one or more terms output by search query rewriter 32.

Index searcher 34 may perform operations to search index 29 to identify terms matching the closest if not identical to the terms of a query output by search query rewriter 32. Based on the closest terms identified by index searcher 34, index searcher 34 may retrieve the data corresponding to the matching terms in the index. Index searcher 34 may provide output data to generator 36 (“text to query generator”). The output data can be the matching terms and/or the data corresponding to the matching terms.

In some embodiments, generator 36 may perform operations to generate a query from input (e.g., text-based input) received by search service 30. The query may be constructed or assembled based on the matching terms identified by index searcher 34.

Query subsystem 180 may perform operations to selectively determine one or more options for displaying data responsive to the constructed query. For example, query subsystem 180 may determine a visual type for best representing the data responsive to the query. The visual representation may be selected using techniques disclosed herein, such as those described with reference to FIGS. 9-17.

Query subsystem 180 provides the generated query to presentation server 38. Query subsystem 180 may provide the selected visual representation type to presentation server 38. Presentation server 38 may perform operations to request BI server 40 to retrieve data for the query provided by generator 36. BI server 40 may identify and retrieve data responsive to the query received from presentation server 38. BI server 40 provides the retrieved data to presentation server 38, which generates a graphical interface that provides a visual representation of the retrieved data. The visual representation may be determined based on the type of visual representative indicated by generator 38.

IV. Generating a Data Index

FIG. 4 illustrates a flow diagram of a process 400 for generating an index for data according to an embodiment. Specifically, process 400 may be implemented by crawl subsystem 152. A crawl process may be implemented using techniques disclosed in U.S. patent application Ser. No. 14/861,893, filed Sep. 22, 2015 and entitled “SEMANTIC SEARCHES IN A BUSINESS INTELLIGENCE SYSTEM” and U.S. Provisional Application No. 62/055,402, filed Sep. 25, 2014 and entitled “SEMANTIC SEARCHES IN A BUSINESS INTELLIGENCE SYSTEM.” For example, Appendix A (Section 4 and 5) of U.S. Provisional Application No. 62/055,402, filed Sep. 25, 2014 and entitled “SEMANTIC SEARCHES IN A BUSINESS INTELLIGENCE SYSTEM” may include crawling and indexing of a semantic layer and client interfaces for performing such operations. Crawl subsystem 152 may implement process 400 to generate a data index (e.g., data index 142) from one or more data sources. An example of data index 142 is described below with reference to FIG. 6. As explained above, data model 146 of FIG. 1 may include a logical mapping for data index 152. The logical mapping may be used by data analytic system 150 to determine a semantic meaning of terms in an input string. Data index 142 may include information about one or more subject areas of data from a corpus of data obtained from one or more data sources. Each subject area may have one or more words, or terms (e.g., entities or terms) having meaning that define data in the subject area. The terms may be associated with one or more attributes about the data for the term, such as a measure, a sum, and other attributes defining a type of information stored for the term. In some embodiments, the attributes may be pre-defined for data associated with the term.

Process 400 may be performed with access permitted at a highest level or greatest level of access for all users of data analytic system 150. For example, the access may be defined for an enterprise with respect to enterprise computer system 160. By generating the data index based on all types of access permitted, the data index can be generated for all data accessible to user for all different levels of access.

Process 400 may be operated based on a configuration defined for data analytic system 150. Process 400 may perform one or more operations (e.g., a crawl) to generate data index 142. The operations may be configured according to a crawl configuration.

Process 400 may include step 402 for determining a crawl configuration. A crawl configuration may be determined at design-time or run-time of data analytic system 150. The crawl configuration may be generated based on input by a user. The input may be received from one or more graphical interfaces, such as those described any of the interfaces disclosed in U.S. patent application Ser. No. 14/861,893, filed Sep. 22, 2015 and entitled “SEMANTIC SEARCHES IN A BUSINESS INTELLIGENCE SYSTEM” and U.S. Provisional Application No. 62/055,402, filed Sep. 25, 2014 and entitled “SEMANTIC SEARCHES IN A BUSINESS INTELLIGENCE SYSTEM.” The crawl configuration may include information indicating when to conduct a crawl, how to conduct a crawl, what data sources to read for a crawl, and any other information for performing one or more crawls to generate data index 142. In some embodiments, a crawl configuration may be generated based on user input received at design-time. Step 402 may be performed at run-time during which the crawl configuration is determined by being read.

Process 400 may perform operations 404 to generate data index 142. Data index 142 may include one or more indices, each specific to a different language. Process 400 may perform step 406 to create an index for each different language. A data structure may be generated as an index for a language. An index for a language may include data for multiple different subject areas from one or more different data sources. Creating an index for a language may include performing operations 408 to index different subject areas for the language. For a language, different subject areas may be identified. The steps for process 400 may be performed in part by requesting information from intelligence server 40. Intelligence server 40 may have a physical data mapping of data for one or more data sources. As such, crawl subsystem 152 can communicate with intelligence server 40 to obtain data to generate data index 142.

To perform subject area indexing 408, process 400 may perform step 410, step 412, and operations for column indexing 414. Operations for subject area indexing 408 may be performed for each different subject area. At step 410, data about one or more subject areas is obtained from one or more data sources. For example, one or more data sources may be defined for a particular subject area. The data may include information indicating each of the subject areas, which may cover multiple different industries, departments, products, services, or other aspects in an enterprise. Information about the subject area may be obtained by issuing one or more queries, such as a logical query to identify the subject areas accessible to all users. The query may be communicated to an intelligence server to obtain the subject area mapped from data from one or more data sources. At step 412, a subject area index is generated based on the data obtained for a subject area. The index may be generated as a data structure. Subject area indexing 408 may be repeated for each of the different subject areas identified for a language.

Operations 414 for column indexing may be performed to generate a column index for multiple columns in a subject area. Operations 414 for process 400 include step, 416, step 418, step 420, step 422, and step 424. At step 416, process 400 includes obtaining column information for a subject area. Each subject area may have one or more terms, which are words having a meaning for the subject area. Each column may correspond to a different attribute for terms in the subject area. A column index may be created for each column corresponding to a set of terms for a subject area. Examples of attributes may include a dimension, or facts (e.g., a measure or a value) for the terms having the attribute. Terms may define a subject area. A term could be an attribute associated with a value (e.g., a measure or a metric) or a dimension that describes an aspect of a subject area. A column may be defined based one or more terms, each having an attribute that defines the terms for the column.

Steps of 418, 420, and 422 may be performed for each column index to be generated for column indexing operations 414. At step 418, one or more column indices are generated. A column index may be generated as a data structure. The data structure can be associated with a data structure (e.g., subject area index) storing information about the subject area for which the column index is generated. A column index may be generated for each column identified in the column information. At step 420, a query may be executed to obtain column data for each column index. The query may be a logical query to obtain all data accessible for all levels of access. The column data for a column index may be stored at step 422. Column indexing operation 414 may be repeated for each column index.

Upon completion of column indexing 414, process 400 may perform operations for subject area indexing for one or more subject areas that were identified in an index for a language. Process 400 may proceed to step 410 for subject area indexing.

If no subject areas remain for indexing, process 400 may proceed to end at which point an index for a language has been generated by process 400. Process 400 may be repeated to create an index for different languages and/or to capture updates to an existing language. All or some of the steps in process 400 may be repeated to capture changes or updates to an index, such as the addition of one or more subject areas and/or columns for a subject area.

FIG. 5 illustrates a detailed diagram of a query system 500 in accordance with an embodiment. Query system 500 may be implemented for query subsystem 180. Query system 500 receives an input string 502 from an interface of an application providing a query service. The interface may be provided at a client system. The input string may include one or more strings of characters. Each string may have one or more characters. One or more strings of characters may be identified as words. Collectively, the input string 502 may represent a query for data or may have a meaning intended as an inquiry for data. Query system 500 may perform processing on input string 502 to determine a meaning from the input string 502. In determining the meaning, query system 500 may perform processing to determine a semantic meaning of the input string 502.

Query system 500 may formulate a query based on processing the input string 502. Query system 500 may produce output data 504. Query system 500 may communicate output data 504 to a computer system (e.g., an enterprise computer system) that can perform the query against one or more data sources. The computer system may provide a presentation service to that can generate a graphical visualization for presentation of results produced for the query. In some embodiments, query system 500 may determine a best-suited visualization for displaying the results of the query. Output data 504 may include information indicating the visualization for displaying the results of the query.

Query system 500 includes one or more subsystems and/or modules, such as those described with reference to FIG. 1 including input handler 182, index manager 184, query generator 186, and visualization manager 188.

Input handler 182 may perform operations described with reference to search query rewriter 32 of FIG. 3. Input handler 182 may perform pre-processing of the input string to identify words to be processed for generating a query. Input handler 182 may include one or more subsystems and/or modules, such as text parser 510, term stemmer 512, term combiner 514, and term reference handler 516. The operations performed by input handler 182 may be performed in many different orders. The examples described with reference to input handler 182 is merely one example of a set of operations performed by input handler 182.

Text parser 510 may perform operations to process input string into one or more subs-strings, each having one or more characters. For example, text parser 510 may perform one or more known algorithms to process the input string to identify unique strings. Input string 510 may be processed based on white spaces, hyphens, or other characters to identify different words or phrases. In some embodiments, text parser 510 may tokenize an input string for further processing by input handler 182. Input string 510 can be processed to remove (e.g., trim) articles and other strings that do not have meaning in the language of the input string. Text parser 510 may determine a root word for each of the words in input string 510. Text parser 510 may generate a data structure (e.g., an array) including each of the strings identified in input string 502. The strings may be stored in an order or sequence as identified in input string 502 to preserve a meaning of input string 502. The output of text parser 510 may be a data structure of strings. Each of the strings may represent one or more words or terms (e.g., characters having a meaning). In some embodiments, a string in the output may have multiple words, or terms.

In some embodiments, term reference handler 516 may perform operations to identify a string with a word that is a reference word, or term. Term reference handler 516 may perform operations according to a sequence of one or more steps to match a term to a known, or reference term, if existing. One or more comparisons of a term may be performed to determine if a reference term exists. For example, a root term identified by text parser 510 may be compared to reference terms. A reference term may be identified as such if it matches a term in a reference store 140 (e.g., a reference dictionary), which stores terms that have a meaning with other terms. The terms in reference store 140 may be pre-defined. Some terms in reference store 140 may have a meaning within the scope of a subject area or domain of knowledge. Specifically, term reference handler 516 may determine whether any string in input string 502 has a meaning to any other term based on the terms in reference store 140. FIG. 7 illustrates examples of terms that indicate a meaning with respect to other terms.

Term reference handler 516 may process terms in input string 502 to identify one or more of terms matching with a term in reference store 140. Based on the identified term, term reference handler 516 may examine each of the string(s) following the identified term to determine whether any word in the strings satisfy the hint with respect to the identified term. The reference store 140 may indicate an action as to how the identified terms and the terms identified by the hint are to be interpreted. Term reference handler 516 may perform processing initially before index manager 184 performs processing to identify words that have meaning. The terms identified based on term reference handler 516 may be stored with information indicating the meaning identified from those terms. For example, the data structure of strings may be modified to identify those strings having a reference terms and other terms related to the reference term(s). The identified terms may be removed from the array of terms for further processing. In some embodiments, input handler 182 may implement natural language processing (NLP) techniques to further identify meaning of one or more of the terms. Terms that have an identified meaning can be removed from the terms for processing using data index 142.

Term stemmer 512 can perform operations to process strings identified by text parser 510. Term stemmer 512 may perform processing to identify a word stem, a word base, or root form of a term in a string. Term stemmer 512 may perform operations on terms in any of the strings identified by text parser 510. One or more known techniques for stemming may be implemented to identify a root form of a term. For terms in a specific domain or subject area, reference store 140 may be used to identify a root form of a word. The data structure of strings may be modified such that the terms may be further modified to include the root form of those terms.

Term combiner 516 may perform processing to identify whether any of the terms in input string 502 can be combined or have a relationship with each other. For example, the terms identified from the operations described above for input handler 182 may processed by term combiner 516. Reference store 140 may store information about terms in a particular subject area or domain. The information may indicate a meaning or a relationship between the terms. Reference store 140 may include terms such as synonyms, business terms, user-defined terms, industry terms, or any other types of terms not ordinarily defined in a language of the input string. Term combiner 516 may identify terms as being part of a name or having a meaning in a particular subject area defined by a group of users. Term combiner 516 may determine a meaning of one or more terms of input string 502 based on identification of a synonym or relationship with one or more terms of a group identified in reference store 140. Term combiner 516 may store information about a meaning of one or more terms identified using reference store 140.

Input handler 182 produces output data for processing by index manager 184. The output data may include a data structure of strings identified in input string 502. The strings may be modified or associated with data based on operations performed by input handler 182, such as stemming, trimming, parsing, and identification of terms that can be combined and/or have meaning with other terms in input string 502.

Index manager 184 performs operations based on data output by input handler 182. Index manager 184 may determine a measure of comparison (e.g., a comparison score) for the terms compared with data index 142. The measure of comparison may indicate a relevancy ranking score of each terms and/or the terms collectively as they compare to different subject areas, and the terms within those subject areas. The measure of comparison may be useful for determining a meaning of terms in the input string 502. A semantic meaning may be determined based on the meaning of the terms in the input string 502. Some or all operations performed by input handler 18 may be performed before index manager 184. Specifically, index manager 184 may perform operations described with reference to index searcher 34 of FIG. 3. Index manager 184 may implement operations according to algorithms and techniques described with reference to index search 34 of FIG. 3 and with reference to FIG. 6.

Index manager 184 can determine a measure of comparison for terms in input string 502 as they compare to terms in different subject areas of data index 142. Data index 142 may include an index of terms for one or more subject areas or domains. Using data index 142, index manager 184 can determine a closest match of terms in input string 502 to a subject area to determine a meaning of input string 502. The meaning of the terms in input string 502 can be used to construct a query using the terms. Specifically, the terms in output data produced by input handler are compared against terms in different subject areas to determine the best matching subject area. A meaning of the terms may be inferred based on the matching terms in a subject area. An order of the terms may be identified based on a meaning of the terms determined from comparison with data index 142.

Terms that are combined may be compared to data index 142 according to the combination. Terms that were identified as having a meaning with other terms may not be compared to data index 142. In some embodiments, not all terms having a meaning identified by term reference handler may be compared to data index 142. The terms that are not used to infer the meaning on other terms may be compared to data index 142 to determine a collective meaning of all terms in input string 502.

Index manager 184 may generate a data structure of terms based on comparison of terms with data index 142. A measure of comparison (e.g., a relevancy ranking score) may be included with information (e.g., compared terms) from data index 142 used to generate the measure of comparison. Some terms may be grouped together based on a comparison of those terms as a group. In some embodiments, the data structure generated by index manager 184 may include values that indicate a measure of comparison for the terms compared with data index 142. The measure of comparison may be provided by a top number (n) matches with data index 142. The measure of comparison may include the measures computed for each of the different data structures in data index 142 described with reference to FIG. 6.

Query generator 186 may perform operations including those described with reference to generator 36. The operations performed by query generator 186 may include generating a query based on the data generated by input handler 182, and index manager 184. Query generator 186 may assess the output of input handler 182 and index manager 184 to determine a semantic meaning of terms in input string 502. Specifically, query generator 186 may consider the measure of comparison of terms, the terms identified by input handler 182, and other meaning determined by input handler 182 in determining a meaning of terms in input string 502.

Query generator 186 may determine a semantic meaning of the terms identified by input handler 182. Specifically, query generator 186 can assess a meaning of terms based on the measure of comparison, which can be used to determine how close a term matches a term in data index 142. A meaning for each of the terms individually or collectively can be inferred based on the closest matching terms according to the measure of comparison. The terms may be considered in an order or sequence of input string 502. Query generator 186 may determine a meaning of terms collectively based on the subject area having terms that closely match the terms in input string 502. Some terms may be identified as a dimension for the subject area. Some terms may be identified as an attribute for which to limit a search in the subject area for one or more dimensions. As further described below, a query may be generated for the terms being considered collectively based on the measure of comparison using data index 142.

Query generator 186 may further infer meaning based on terms that are combined by term combiner 514 and/or terms that are grouped as having a closest match to a subject area. Query generator 186 may further consider a relationship of terms determined by term reference handler 516. In some embodiments, terms for input string 502 may be further considered in combination with the hint for a reference term. Query generator 186 can generate a query according to the considerations described above. Techniques for generating a query based on an input string are described below.

A query may be generated in many different languages, which are structured, semi-structured, or unstructured. The format of a query may be defined by a search system of an intelligence service provided by intelligence server 170. For example, the format of a query may be generated as a logical SQL query defined by a computer system providing the intelligence service. In some embodiments, query generator 186 may output a query as output data 504.

In some embodiments, output data 504 may include data indicating one or more visual representations for displaying results of the query generated by query generator 186. The output data 504 may indicate the visual representation best-suited for displaying the results. Visualization manager 188 may determine a visual representation for displaying results of the query. The visual representation may indicate information for determine a graphical interface to display the results of a query. The visual representation may indicate information for producing a graphical display, such as a format, a layout, or a diagram (e.g., a chart or a diagram). The visual representation may be determined based on user-defined preferences, user behavior, and/or type of data to display. Described below are examples of techniques for determining a visual representation for displaying results and examples of visual representations.

V. Data Index

FIG. 6 illustrates data structures of a data index 600 according to some embodiments. Data index 600 is an example of data index 142 of FIG. 1. Data index 600 may be generated by a process, such as one described with reference to FIG. 4. Data index 600 may be defined by multiple documents such as those shown in FIG. 6. Each of the documents may have different formats. A data structure may be generated on the format of the document. Data index 600 provides a logical mapping of data identified by crawling one or more data sources. The mapping may be useful to determine a meaning of terms for a query.

Data index 600 may be defined by a hierarchical data structure. Data index 600 may be used by data analytic system 150 to determine a semantic meaning of an input string. The semantic meaning may be used to generate a query for data based on the meaning.

In at least one embodiment, data structure 620 may be generated for one or more documents about subject areas (“subject area documents”). Data structure may represent a global subject area index for different subject areas or domains of information. Each subject area may be identified by crawling one or more data sources. A subject area may be identified as a topic or subject area for presentation (“presentation subject areas”) as disclosed by techniques in U.S. patent application Ser. No. 14/861,893, filed Sep. 22, 2015 and entitled “SEMANTIC SEARCHES IN A BUSINESS INTELLIGENCE SYSTEM” and U.S. Provisional Application No. 62/055,402, filed Sep. 25, 2014 and entitled “SEMANTIC SEARCHES IN A BUSINESS INTELLIGENCE SYSTEM.” In the example of FIG. 6, data structure 620 is created for multiple subject area documents, each represented by a record associated with a document identifier (“DocID”). Each subject area in a record may have a subject area name (“Subject Area”) and one or more columns (“Subject Area Columns”). Each subject area column may correspond to one or more words, or terms, identified as having a meaning for the subject area. The terms of a subject area may be pre-defined or defined based on data stored for the subject area. Each term may be associated with a column identifier (e.g., column index), which can be used to reference a data structure with information about columns for a subject area.

Data structure 640 may be generated for one or more documents about columns for a subject area (e.g., subject area columns). Each subject area column may be identified as a presentation column having data for the subject area. Data structure 640 may include a document identifier (“DocID”) for each document corresponding to a subject area column. The subject area column in data structure 640 may be referenced by a column identifier in data structure 620. Data structure 640 may include a record for each subject area column associated with a subject area. Each subject area column may be associated with a term for which data can be presented for a subject area. Data structure 640 may represent column indexes for each subject area.

Data structure 660 may be generated for values of each subject area column. Data structure 660 may store values for each column index. Data structure 640 may include a document identifier (“DocID”) for each document corresponding to a subject area column. Each subject area column may have one or more attributes for the column. Examples of attributes may include a value of the subject area column and dimensions, or dimension members for the column. The dimension may be used for determining how to display values for a column of a subject matter. Each subject area column may be identified based on the subject area column index. A dimension may be a factor that limits a scope of a measure for an attribute.

VI. Reference Index

FIG. 7 illustrates a reference data structure 700 according to some embodiments. Reference data structure 700 may be a reference data index in reference store 140. Reference data structure 700 may be defined with one or more keywords that have meaning when combined with other strings of characters (e.g., words) in an input string. The keywords may have meaning within a subject area. Each record in data structure 700 may correspond to a different keyword and an associated hint. One or more hints may be associated with a keyword to determine whether any words following the keyword have meaning in relation to the keyword. The keywords may be defined with respect to a subject area. Each of the keywords may be defined with respect to a particular subject area, or may have a specific meaning in a subject area. As such, multiple reference indices may be defined for different subject areas, where the keyword has a different meaning. The reference index may be chosen for a particular subject area.

Input handler 182 may use data structure 700 to identify strings of characters, such as words or terms, having meaning in relation to any keywords identified in an input string. Those words identified as having meaning based on an identified keyword, may be stored in association with the keyword for consideration later. Input handler 182 can examine words in a string to determine whether any satisfy the hint with respect to the keyword. Upon identifying a closely, or identically matching term in data index, the data for the term can be considered for display in view of the meaning of the keyword.

VII. Semantic Analysis

FIG. 8 illustrates a process 800 for determining a semantic meaning of an input string according to some embodiments. Process 800 can be implemented by query subsystem 180 of FIG. 1.

Process 800 may include step 802 in which an input string (e.g., input string 402) is received. The input string may be based on input by a user. The input may be received through an interface disclosed herein. The input string may be a search query for data from one or more data sources.

At step 804, the input string may be processed. For example, the input string may be processed according to operations described with reference to input handler 182. The input string may be processed to identify one or more terms to compare against data index 142.

In at least one embodiment, processing the input string to identify the plurality of terms includes tokenizing the input string to identify a set of terms based one or more characters. The set of terms may be processed to determine a stem word, if existing, for each of the set of terms. Each of the set of terms may be processed to determine a relationship between any pair of terms in the set of terms. The plurality of terms may be identified based on the processing performed by input handler 182. For example, the plurality of terms may be identified based on the set of terms and the relationship between any pair of terms in the set of terms. The processing may include changing an order of terms in the set of terms. The set of terms may be modified to add or remove terms based on processing by input handler 182.

At step 806, a comparison (e.g., a first comparison) is performed between one or more terms identified in the input string to each of the terms of different subject areas, such as data structure 620 of FIG. 6. The terms may be compared to the terms in the subject area columns for each subject area in a subject area index of data index 142. The subject area index may include terms for all subject areas accessible to all users, not just the access permitted to the user that provided the input string.

Terms may be compared based on one or more factors, such as user attributes, term matching, etc. The first comparison may be a measure of comparison (e.g., a score) determined for comparison of the terms in the input string with terms in the subject area. The measure may be weighted based on one or more criteria. A value (e.g., a score) may be determined for each subject area. The score for a subject area may be determined based on a number of terms matching the subject area. Comparing the terms may include determining a measure of comparison of the terms with the subject areas. The terms may be compared in any order. However, some terms may be identified with an order based on a reference term indicating a meaning of multiple terms. In some embodiments, the comparison may be performed based on one or more algorithms for similarity analysis. The measure of comparison may be calculated based on one or more measures of determining relevancy. For example, the comparison may be performed using term frequency-inverse document frequency (TFIDF). The measure of comparison may indicator a relevant value of comparing a term to the subject areas.

At step 808, a set of subject areas are selected based on having a score reflecting a closest (e.g., a best or greatest) match with terms. The set of subject areas may be selected as a set from the subject index as having a closest match with the terms in the input string. The set may be a top number (n) of subject areas. A closest match may be based on one or more attributes of the first comparison. Attributes may include the number of terms matching between the input string and terms in a subject area. In one example, the top three subject areas may be selected as having the closest number of matching terms. The subject areas may be selected by having a greatest number of terms matching, or terms having a closest match.

At step 810, a subject area column index is prepared for the subject areas selected at step 808. An example of a subject area column index is data structure 640 of FIG. 6. The subject area column index may include attributes for the terms, such as a measure (e.g., a dimension), a sum, or other attributes about the term for which data exists. In some embodiments, a subject area column index may be generated dynamically based on the selected subject areas in subject area index.

At step 812, a comparison (e.g., a second comparison) is performed for the terms identified in an input string to a subject area index for each of the selected subject areas. Performing the second comparison includes comparing each of the plurality of terms to terms in each of the selected set of subject areas of the subject area column index and to one or more attributes of each of the terms in each of the selected set of subject areas. The terms may be compared based on the attributes of those terms to identify the terms for which attributes are requested for a query. Comparing the terms with the subject area column index may include determining a measure of comparison of the terms with each of the terms compared in the subject area column index. The measure of comparison may be a value (e.g., a relevancy score) that indicates how close terms of the input string are to terms in a subject area. For example, the measure of comparison may be a relevancy score. The measure of comparison may be determined using an algorithm such as TFIDF. The measure may be weighted based on one or more factors, such as a number of attributes that match with the term.

At step 814, based on the second comparison, one or more terms may be selected for the terms in the subject area index for each of the selected subject areas. The one or more selected terms may correspond to a threshold number of matches (“hits”) with terms in the subject areas of the subject area column index. For example, a threshold number of matches (e.g., 30 matches) may be selected for the matches that have the highest measure of comparison, e.g., a highest or greatest relevancy score. The threshold number of matches may be across multiple subject areas. The threshold number may be chosen based on a maximum number of matches that can be shown on a display of a client system.

At step 816, values for the subject area column index 816 may be prepared. For example, a data structure, such as data structure 660 of FIG. 6, may be generated (e.g., populated) or data may be retrieved for values for the terms of the subject areas selected at step 814. In some embodiments, data structure 660 may be generated as part of data index 142. Portions of data structure 600 may be identified based on the terms that are selected at step 814.

At step 818, the terms selected at step 814 may be compared to the values of the matching terms in each of the selected subject areas of the subject area column index. The terms may be compared using one or more algorithms, such as TFIDF. The terms may be compared individually and in combination to identify a match of each term or group of terms to the values. The comparing may include determining a measure of comparison, which indicates a value of comparing a term or term(s).

At step 820, one or more matching terms of the selected one or more terms are determined based on the comparing of the selected one or more terms to the value for each of the plurality of terms in the selected set of subject areas. The terms having the best match, if not identical to the terms in the subject area column index, may be identified as having a meaning corresponding to the matched terms. The terms having the best match (e.g., highest value or score), if not identical to terms in the subject area column index, can be selected as the terms defining a query. The terms having the best match may be understood to have a semantic meaning of the terms that match.

At step 822, a query is generated based in determining the one or more matching terms. In some embodiments, a query (e.g., a logical SQL query) may be generated based on the terms determined at step 820. Based on identifying terms being in a particular subject area and having a relationship with other terms, a query can be constructed based on the meaning of the terms determined from the input string. Terms may be arranged based on their best match with a term in the subject area column index. Each of the matching terms may be determined as a best match based on a measure of comparison to a value of a terms in the subject area column index. An order of the matching terms may be determined based on the meaning of the terms determined based each of their best matches to a term in the subject area column index.

Process 800 may proceed to end at step 824. Process 800 can be performed for multiple different input strings. In some embodiments, process 800 can be performed for an input string as it is formed. Process 800 may be repeated as the input string changes.

VIII. Graphical Visualization of Search Results

FIG. 9 illustrates a diagram of a data structure 900 for determining a visualization of data according to some embodiments. Data structure 900 is an example of information (e.g., a visual presentation mapping) used by data analytic system 150 to determine a visual representation best suited for displaying results of a query determined by semantic analysis of an input string by a user. Client systems, such as a mobile device, are limited in display of information due to a screen size that is smaller than other client systems. It may be desirable to display search results in a manner better suited for display at a client system having limited display space. It may be further desirable to track a user's behavior with respect to a subject area to determine a visual representation that may be best suited for the user based on previous results displayed.

The information in data structure 900 may be created in many ways. Some or all of the information in data structure 900 may be pre-defined based on the different terms to display in a subject area. A different data structure 900 may be created for different users or other criteria. In some embodiments, a different data structure such as data structure 900 may be created for different subject areas, and within a subject are, different terms. Data structure 900 may be modified based on user preferences or behavior.

In at least one embodiment, data structure 900 may indicate options for visual representation of data responsive to a query. Data structure 900 may be defined by one or more axes, each of which is a factor in determining a visual representation for displaying data. For example, data structure 900 has one axis of data structure 900 defined based on one or more measures, e.g., a metric or a value, each corresponding to a term matching an input string. The visual representation (e.g., a pie chart, a scatter/bubble chart, a bar chart, a bubble chart, or a table) may change based on the number of measures. In the example of FIG. 9, data structure 900 may be defined based on another axis of dimensions, corresponding to a dimension for an attribute of a term. The visual representation may change based on the number of dimensions for a term. A dimension may be an attribute that defines or limits a term measure. In the example of FIG. 9, a visual representation may be selected based on the number of measures and the number of dimensions identified for a term having the measures. For a term defined by multiple measures and multiple dimensions, a visual representation such as a bubble chart or a table might be useful to see multiple axes of information. Data analytic system 150 may select a visual representation using data structure 900. The visual representation may be provided to presentation server 168 to generate a graphical interface according to the visual representation.

U.S. Provisional Application No. 62/356,471, filed Jun. 29, 2016 and entitled “TECHNIQUES FOR SEMANTIC SEARCHING,” and U.S. Provisional Application No. 62/396,088, filed Sep. 16, 2016 and entitled “TECHNIQUES FOR SEMANTIC SEARCHING,” including Appendix C, discloses examples of different graphical interfaces for displaying results of queries submitted to data analytic system 150 for a query service. The examples show different types of visual representations of results for a different query. Each of the examples includes a data structure in a formatted language defining the visualization for a display of results based on measures and dimensions. Different visual representations of data are displayed based on the number of measures and dimensions for results. The example data structures in Appendix C of U.S. Provisional Application No. 62/356,471, filed Jun. 29, 2016 and entitled “TECHNIQUES FOR SEMANTIC SEARCHING,” and U.S. Provisional Application No. 62/396,088, filed Sep. 16, 2016 and entitled “TECHNIQUES FOR SEMANTIC SEARCHING,” may be generated as a visual representation of results for a query. In some embodiments, all or some of the data structures may be generated by data analytic system 150. The visual representation may be communicated to a presentation service to determine a display based on the data structure for a visual representation. Each of the data structures may include the measures, dimensions, formatting, display characteristics, and other information indicating a relationship of data for display.

FIGS. 10-17 illustrate graphical interfaces for searching and viewing search results according to an embodiment. Appendices A and B describe some embodiments with references to FIGS. 10-17. Specifically, the graphical interfaces shown in FIGS. 10-17 may be generated by data analytic system 150, by a presentation service provided by a presentation server (e.g., presentation server 168), or a combination thereof. The graphical interfaces may include a visual representation that is determined by data analytic system 150 based on a query generated after semantic analysis of an input string. In some embodiments, data analytic system 150 may provide information about the visual representation to a presentation server to generate a graphical interface. In at least one embodiment, a user may perform a process for searching using a graphical interface providing access to a query service provided by data analytic system 150. The process for searching may be performed as described with reference to “Search,” such as section 5.1.3 in Appendix A of U.S. Provisional Application No. 62/055,402, filed Sep. 25, 2014 and entitled “SEMANTIC SEARCHES IN A BUSINESS INTELLIGENCE SYSTEM.” The process may be explained further below with reference to FIGS. 10-17.

In some embodiments, the graphical interfaces disclosed herein may be provided by a presentation server to a client system. The graphical interface may be provided for a query service by data analytic system 150. In several of the examples show below, a graphical interface may enable a user to provide input for the query service to query data. The graphical interface may be modified to show a visual representation of the data responsive to the query. In some embodiments, a graphical interface may be presented to enable a user to further refine input for a query. For example, the graphical interface may provide options for input based on the input already provided to the graphical interface. In another example, the graphical interface may provide one or more interactive elements to select a visual representation and/or refine input for a query.

Responsive to input in a graphical interface, data analytic system 150 may perform further processing to determine possible options for input based on input already provided. The presentation server may provide the input at run-time to data analytic system 150 which can then perform processing including semantic analysis of current input to determine possible options for further input. In some embodiments, data analytic system 150 may determine results responsive to the current input data and send the results to the presentation server to display in the graphical interface. Data analytic system 150 may perform a search based on input and cause the presentation server to display the results for the input in a manner best-suited for the query. In other words, the graphical interface may be dynamic such that it is updated to display results responsive to input as new input is entered. The graphical may present different options for visualization as the result for input changes (e.g., one or more measure or dimensions are identified in a search result). Data analytic system 150 may communicate with a presentation server to receive input from a user via a graphical interface and to perform query processing and adjust a display of results responsive to the input from the user.

In FIG. 10, graphical interface 1000 may be displayed at a client system. Graphical interface 1000 may include an interactive element 52 to specify search input such as “revenue.” Data analytic system 150 may process the input to determine a query to construct based on the input. For example, data analytic system 150 rewrite the input it into a query language form, such as logical SQL. The logical SQL may be communicated to an intelligence server to obtain data responsive to the query. A visual representation for the query is selected and displayed for the query. Using data structure 900, data analytic system 150 can determine a visual representation for displaying results of a query for a single term (“revenue”) having a single dimension.

FIG. 11 illustrates a graphical interface 1100 is shown after a user further enters input such as “2010” for revenue. Data analytic system 150 may perform a query process to parse the input and determine a semantic meaning of the input. A list of matching terms may be identified by data analytic system 150 and shown to user in graphical interface 1100 as a drop down list 56 of suggested other parameters. The terms may be listed in order of best possible match. The terms may be identified based on their association with terms in a subject area matching the terms in the input. As mentioned above, the search process may document and save the search terms and their associated queries for future use. In one embodiment, they are stored by user, so the system can access a list of terms used by the user and the statistics of the most frequently used terms. Accessing this list would allow the system to populate the drop down list 56.

FIG. 12 illustrates a graphical interface 1200 of a visual representation of results in response to the additional input of “product” with the input described in FIG. 11. The system may process the new input string to identify its meaning and generate a query to obtain new results. The new results may be displayed in a different visual representation based on the new measures and dimensions in the input. In some embodiments, a visual representation may include additional interactive elements to selectively filter data by one or more dimensions, such as different types of devices illustrated in graphical interface 1200. The dimensions may be identified as options based on their match in a subject area identified for the input.

FIG. 13 illustrates a graphical interface 1300 of a visual representation of results in response to the additional input of “target revenue” with the input described in FIG. 12. As the system presents visualizations, it can also offer other formats for the user, such as visual representations 62. Visualizations may be chosen based on the measures and dimensions, using the data structure described with reference to FIG. 9. In some cases, such as FIG. 14, a user may type in the terms ‘chart’ in the search bar. The system would then present the appropriate visualization or options for choosing a visualization. The system can use this as another opportunity to learn about the user and gather statistics as to what types of visualizations the user prefers. This particular user may prefer pie charts, so the system may default to presenting information to the user in pie charts.

In FIG. 13, for example, the additional element of “target revenue” is added. There are now four elements to the user's search, each of which will have its own query statements, results and conversion to visualization. The user may have different visualization depending upon the number of elements of the search, or the nature of the search. He or she may prefer comparative bar charts as shown in FIG. 13 when there is a ‘target’ or ‘projected’ or the term ‘versus’ in the original search request. The system gathers all of this information and accesses it when the user logs in and performs a search. The text-to-query generator can retrieve it from the BI server and use it to generate the visualizations. Side bar 62 displays other visualization options to the user. If the user starts selecting one of the other charts more frequently, the system will gather this statistic as well and use it to learn the user's preferences.

FIG. 14 shows graphical interface 1400, which is an alternative view for presentation to the user. One should note that the sidebar only shows this type of chart and pie charts as options. This may be based upon the user's preferences as noted above, or may be based upon the level of complexity of the query. However, this does not limit the user to those particular types. Under the term ‘chart’ in the search bar, as shown in graphical interface 1500 of FIG. 15, the pull down list allows the user to select one of many different charts. As the user uses the system and this information is gathered, the system will start to be more accurate in matching the offered visualizations with the user's preference, making the system more efficient and user friendly.

FIG. 16 illustrates graphical interface 1600, which shows the resulting line bar chart, with other line bar charts offered to the right. If the user decides that he or she does not want to see the discount amount that is included with the chart, highlighted in FIG. 16, the user can remove it. Each of the search terms in the search bar will have their own pull down list to allow the user to change or delete that term. In FIG. 17 illustrates a graphical interface 1700 that shows an example in which the user as removed the discount amount term from the search bar. The removal of a term is not necessarily depending upon the other terms in the search bar.

One should note that the above user interfaces were shown in ‘desktop’ form, but will have mobile versions as well. Using this system, a relatively unsophisticated user can access the wealth of data stored in their organizations business intelligence system. The system can gather statistics on the user's interaction with the system to allow it to offer suggestions as to search terms, visualizations, reports, etc. The storage of the resulting searches and visualizations will also allow the system to have access to already formatted query statements and other information to make it run more efficiently.

IX. Computing Environments

FIG. 18 depicts a simplified diagram of a distributed system 1800 for implementing an embodiment. In the illustrated embodiment, distributed system 1800 includes one or more client computing devices 1802, 1804, 1806, and 1808, which are configured to execute and operate a client application such as a web browser, proprietary client (e.g., Oracle Forms), or the like over one or more network(s) 1810. Server 1812 may be communicatively coupled with remote client computing devices 1802, 1804, 1806, and 1808 via network 1810.

In various embodiments, server 1812 may be adapted to run one or more services or software applications. In certain embodiments, server 1812 may also provide other services or software applications can include non-virtual and virtual environments. In some embodiments, these services may be offered as web-based or cloud services or under a Software as a Service (SaaS) model to the users of client computing devices 1802, 1804, 1806, and/or 1808. Users operating client computing devices 1802, 1804, 1806, and/or 1808 may in turn utilize one or more client applications to interact with server 1812 to utilize the services provided by these components.

In the configuration depicted in FIG. 18, software components 1818, 1820 and 1822 of system 1800 are shown as being implemented on server 1812. In other embodiments, one or more of the components of system 1800 and/or the services provided by these components may also be implemented by one or more of the client computing devices 1802, 1804, 1806, and/or 1808. Users operating the client computing devices may then utilize one or more client applications to use the services provided by these components. These components may be implemented in hardware, firmware, software, or combinations thereof. It should be appreciated that various different system configurations are possible, which may be different from distributed system 1800. The embodiment shown in FIG. 18 is thus one example of a distributed system for implementing an embodiment system and is not intended to be limiting.

Client computing devices 1802, 1804, 1806, and/or 1808 may include various types of computing systems. For example, a client computing device may include portable handheld devices (e.g., an iPhone®, cellular telephone, an iPad®, computing tablet, a personal digital assistant (PDA)) or wearable devices (e.g., a Google Glass® head mounted display), running software such as Microsoft Windows Mobile®, and/or a variety of mobile operating systems such as iOS, Windows Phone, Android, BlackBerry 10, Palm OS, and the like. The devices may support various applications such as various Internet-related apps, e-mail, short message service (SMS) applications, and may use various other communication protocols. The client computing devices may also include general purpose personal computers including, by way of example, personal computers and/or laptop computers running various versions of Microsoft Windows®, Apple Macintosh®, and/or Linux operating systems. The client computing devices can be workstation computers running any of a variety of commercially-available UNIX® or UNIX-like operating systems, including without limitation the variety of GNU/Linux operating systems, such as for example, Google Chrome OS. Client computing devices may also include electronic devices such as a thin-client computer, an Internet-enabled gaming system (e.g., a Microsoft Xbox gaming console with or without a Kinect® gesture input device), and/or a personal messaging device, capable of communicating over network(s) 1810.

Although distributed system 1800 in FIG. 18 is shown with four client computing devices, any number of client computing devices may be supported. Other devices, such as devices with sensors, etc., may interact with server 1812.

Network(s) 1810 in distributed system 1800 may be any type of network familiar to those skilled in the art that can support data communications using any of a variety of available protocols, including without limitation TCP/IP (transmission control protocol/Internet protocol), SNA (systems network architecture), IPX (Internet packet exchange), AppleTalk, and the like. Merely by way of example, network(s) 1810 can be a local area network (LAN), networks based on Ethernet, Token-Ring, a wide-area network, the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a public switched telephone network (PSTN), an infra-red network, a wireless network (e.g., a network operating under any of the Institute of Electrical and Electronics (IEEE) 802.11 suite of protocols, Bluetooth®, and/or any other wireless protocol), and/or any combination of these and/or other networks.

Server 1812 may be composed of one or more general purpose computers, specialized server computers (including, by way of example, PC (personal computer) servers, UNIX® servers, mid-range servers, mainframe computers, rack-mounted servers, etc.), server farms, server clusters, or any other appropriate arrangement and/or combination. Server 1812 can include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization. One or more flexible pools of logical storage devices can be virtualized to maintain virtual storage devices for the server. Virtual networks can be controlled by server 1812 using software defined networking. In various embodiments, server 1812 may be adapted to run one or more services or software applications described in the foregoing disclosure. For example, server 1812 may correspond to a server for performing processing as described above according to an embodiment of the present disclosure.

Server 1812 may run an operating system including any of those discussed above, as well as any commercially available server operating system. Server 1812 may also run any of a variety of additional server applications and/or mid-tier applications, including HTTP (hypertext transport protocol) servers, FTP (file transfer protocol) servers, CGI (common gateway interface) servers, JAVA® servers, database servers, and the like. Exemplary database servers include without limitation those commercially available from Oracle, Microsoft, Sybase, IBM (International Business Machines), and the like.

In some implementations, server 1812 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of client computing devices 1802, 1804, 1806, and 1808. As an example, data feeds and/or event updates may include, but are not limited to, Twitter® feeds, Facebook® updates or real-time updates received from one or more third party information sources and continuous data streams, which may include real-time events related to sensor data applications, financial tickers, network performance measuring tools (e.g., network monitoring and traffic management applications), clickstream analysis tools, automobile traffic monitoring, and the like. Server 1812 may also include one or more applications to display the data feeds and/or real-time events via one or more display devices of client computing devices 1802, 1804, 1806, and 1808.

Distributed system 1800 may also include one or more databases 1814 and 1816. These databases may provide a mechanism for storing information such as user interactions information, usage patterns information, adaptation rules information, and other information used by embodiments of the present disclosure. Databases 1814 and 1816 may reside in a variety of locations. By way of example, one or more of databases 1814 and 1816 may reside on a non-transitory storage medium local to (and/or resident in) server 1812. Alternatively, databases 1814 and 1816 may be remote from server 1812 and in communication with server 1812 via a network-based or dedicated connection. In one set of embodiments, databases 1814 and 1816 may reside in a storage-area network (SAN). Similarly, any necessary files for performing the functions attributed to server 1812 may be stored locally on server 1812 and/or remotely, as appropriate. In one set of embodiments, databases 1814 and 1816 may include relational databases, such as databases provided by Oracle that are adapted to store, update, and retrieve data in response to SQL-formatted commands.

In some embodiments, a cloud environment may provide one or more service. FIG. 19 is a simplified block diagram of one or more components of a system environment 1900 in which services may be offered as cloud services, in accordance with an embodiment of the present disclosure. In the illustrated embodiment in FIG. 19, system environment 1900 includes one or more client computing devices 1904, 1906, and 1908 that may be used by users to interact with a cloud infrastructure system 1902 that provides cloud services. Cloud infrastructure system 1902 may comprise one or more computers and/or servers that may include those described above for server 1812.

It should be appreciated that cloud infrastructure system 1902 depicted in FIG. 19 may have other components than those depicted. Further, the embodiment shown in FIG. 19 is only one example of a cloud infrastructure system that may incorporate an embodiment of the present disclosure. In some other embodiments, cloud infrastructure system 1902 may have more or fewer components than shown in the figure, may combine two or more components, or may have a different configuration or arrangement of components.

Client computing devices 1904, 1906, and 1908 may be devices similar to those described above for client computing devices 1802, 1804, 1806, and 1808. Client computing devices 1904, 1906, and 1908 may be configured to operate a client application such as a web browser, a proprietary client application (e.g., Oracle Forms), or some other application, which may be used by a user of the client computing device to interact with cloud infrastructure system 1902 to use services provided by cloud infrastructure system 1902. Although exemplary system environment 1900 is shown with three client computing devices, any number of client computing devices may be supported. Other devices such as devices with sensors, etc. may interact with cloud infrastructure system 1902.

Network(s) 1910 may facilitate communications and exchange of data between client computing devices 1904, 1906, and 1908 and cloud infrastructure system 1902. Each network may be any type of network familiar to those skilled in the art that can support data communications using any of a variety of commercially-available protocols, including those described above for network(s) 1810.

In certain embodiments, services provided by cloud infrastructure system 1902 may include a host of services that are made available to users of the cloud infrastructure system on demand. Various other services may also be offered including without limitation online data storage and backup solutions, Web-based e-mail services, hosted office suites and document collaboration services, database processing, managed technical support services, and the like. Services provided by the cloud infrastructure system can dynamically scale to meet the needs of its users.

In certain embodiments, a specific instantiation of a service provided by cloud infrastructure system 1902 may be referred to herein as a “service instance.” In general, any service made available to a user via a communication network, such as the Internet, from a cloud service provider's system is referred to as a “cloud service.” Typically, in a public cloud environment, servers and systems that make up the cloud service provider's system are different from the customer's own on-premises servers and systems. For example, a cloud service provider's system may host an application, and a user may, via a communication network such as the Internet, on demand, order and use the application.

In some examples, a service in a computer network cloud infrastructure may include protected computer network access to storage, a hosted database, a hosted web server, a software application, or other service provided by a cloud vendor to a user, or as otherwise known in the art. For example, a service can include password-protected access to remote storage on the cloud through the Internet. As another example, a service can include a web service-based hosted relational database and a script-language middleware engine for private use by a networked developer. As another example, a service can include access to an email software application hosted on a cloud vendor's web site.

In certain embodiments, cloud infrastructure system 1902 may include a suite of applications, middleware, and database service offerings that are delivered to a customer in a self-service, subscription-based, elastically scalable, reliable, highly available, and secure manner. An example of such a cloud infrastructure system is the Oracle Public Cloud provided by the present assignee.

Cloud infrastructure system 1902 may also provide “big data” elated computation and analysis services. The term “big data” is generally used to refer to extremely large data sets that can be stored and manipulated by analysts and researchers to visualize large amounts of data, detect trends, and/or otherwise interact with the data. This big data and related applications can be hosted and/or manipulated by an infrastructure system on many levels and at different scales. Tens, hundreds, or thousands of processors linked in parallel can act upon such data in order to present it or simulate external forces on the data or what it represents. These data sets can involve structured data, such as that organized in a database or otherwise according to a structured model, and/or unstructured data (e.g., emails, images, data blobs (binary large objects), web pages, complex event processing). By leveraging an ability of an embodiment to relatively quickly focus more (or fewer) computing resources upon an objective, the cloud infrastructure system may be better available to carry out tasks on large data sets based on demand from a business, government agency, research organization, private individual, group of like-minded individuals or organizations, or other entity.

In various embodiments, cloud infrastructure system 1902 may be adapted to automatically provision, manage and track a customer's subscription to services offered by cloud infrastructure system 1902. Cloud infrastructure system 1902 may provide the cloud services via different deployment models. For example, services may be provided under a public cloud model in which cloud infrastructure system 1902 is owned by an organization selling cloud services (e.g., owned by Oracle Corporation) and the services are made available to the general public or different industry enterprises. As another example, services may be provided under a private cloud model in which cloud infrastructure system 1902 is operated solely for a single organization and may provide services for one or more entities within the organization. The cloud services may also be provided under a community cloud model in which cloud infrastructure system 1902 and the services provided by cloud infrastructure system 1902 are shared by several organizations in a related community. The cloud services may also be provided under a hybrid cloud model, which is a combination of two or more different models.

In some embodiments, the services provided by cloud infrastructure system 1902 may include one or more services provided under Software as a Service (SaaS) category, Platform as a Service (PaaS) category, Infrastructure as a Service (IaaS) category, or other categories of services including hybrid services. A customer, via a subscription order, may order one or more services provided by cloud infrastructure system 1902. Cloud infrastructure system 1902 then performs processing to provide the services in the customer's subscription order.

In some embodiments, the services provided by cloud infrastructure system 1902 may include, without limitation, application services, platform services and infrastructure services. In some examples, application services may be provided by the cloud infrastructure system via a SaaS platform. The SaaS platform may be configured to provide cloud services that fall under the SaaS category. For example, the SaaS platform may provide capabilities to build and deliver a suite of on-demand applications on an integrated development and deployment platform. The SaaS platform may manage and control the underlying software and infrastructure for providing the SaaS services. By utilizing the services provided by the SaaS platform, customers can utilize applications executing on the cloud infrastructure system. Customers can acquire the application services without the need for customers to purchase separate licenses and support. Various different SaaS services may be provided. Examples include, without limitation, services that provide solutions for sales performance management, enterprise integration, and business flexibility for large organizations.

In some embodiments, platform services may be provided by cloud infrastructure system 1902 via a PaaS platform. The PaaS platform may be configured to provide cloud services that fall under the PaaS category. Examples of platform services may include without limitation services that enable organizations (such as Oracle) to consolidate existing applications on a shared, common architecture, as well as the ability to build new applications that leverage the shared services provided by the platform. The PaaS platform may manage and control the underlying software and infrastructure for providing the PaaS services. Customers can acquire the PaaS services provided by cloud infrastructure system 1902 without the need for customers to purchase separate licenses and support. Examples of platform services include, without limitation, Oracle Java Cloud Service (JCS), Oracle Database Cloud Service (DBCS), and others.

By utilizing the services provided by the PaaS platform, customers can employ programming languages and tools supported by the cloud infrastructure system and also control the deployed services. In some embodiments, platform services provided by the cloud infrastructure system may include database cloud services, middleware cloud services (e.g., Oracle Fusion Middleware services), and Java cloud services. In one embodiment, database cloud services may support shared service deployment models that enable organizations to pool database resources and offer customers a Database as a Service in the form of a database cloud. Middleware cloud services may provide a platform for customers to develop and deploy various business applications, and Java cloud services may provide a platform for customers to deploy Java applications, in the cloud infrastructure system.

Various different infrastructure services may be provided by an IaaS platform in the cloud infrastructure system. The infrastructure services facilitate the management and control of the underlying computing resources, such as storage, networks, and other fundamental computing resources for customers utilizing services provided by the SaaS platform and the PaaS platform.

In certain embodiments, cloud infrastructure system 1902 may also include infrastructure resources 1930 for providing the resources used to provide various services to customers of the cloud infrastructure system. In one embodiment, infrastructure resources 1930 may include pre-integrated and optimized combinations of hardware, such as servers, storage, and networking resources to execute the services provided by the PaaS platform and the SaaS platform, and other resources.

In some embodiments, resources in cloud infrastructure system 1902 may be shared by multiple users and dynamically re-allocated per demand. Additionally, resources may be allocated to users in different time zones. For example, cloud infrastructure system 1902 may enable a first set of users in a first time zone to utilize resources of the cloud infrastructure system for a specified number of hours and then enable the re-allocation of the same resources to another set of users located in a different time zone, thereby maximizing the utilization of resources.

In certain embodiments, a number of internal shared services 1932 may be provided that are shared by different components or modules of cloud infrastructure system 1902 to enable provision of services by cloud infrastructure system 1902. These internal shared services may include, without limitation, a security and identity service, an integration service, an enterprise repository service, an enterprise manager service, a virus scanning and white list service, a high availability, backup and recovery service, service for enabling cloud support, an email service, a notification service, a file transfer service, and the like.

In certain embodiments, cloud infrastructure system 1902 may provide comprehensive management of cloud services (e.g., SaaS, PaaS, and IaaS services) in the cloud infrastructure system. In one embodiment, cloud management functionality may include capabilities for provisioning, managing and tracking a customer's subscription received by cloud infrastructure system 1902, and the like.

In one embodiment, as depicted in FIG. 19, cloud management functionality may be provided by one or more modules, such as an order management module 1920, an order orchestration module 1922, an order provisioning module 1924, an order management and monitoring module 1926, and an identity management module 1928. These modules may include or be provided using one or more computers and/or servers, which may be general purpose computers, specialized server computers, server farms, server clusters, or any other appropriate arrangement and/or combination.

In an exemplary operation, at step 1934, a customer using a client device, such as client computing devices 1904, 1906 or 1908, may interact with cloud infrastructure system 1902 by requesting one or more services provided by cloud infrastructure system 1902 and placing an order for a subscription for one or more services offered by cloud infrastructure system 1902. In certain embodiments, the customer may access a cloud User Interface (UI) such as cloud UI 1912, cloud UI 1914 and/or cloud UI 1916 and place a subscription order via these UIs. The order information received by cloud infrastructure system 1902 in response to the customer placing an order may include information identifying the customer and one or more services offered by the cloud infrastructure system 1902 that the customer intends to subscribe to.

At step 1936, the order information received from the customer may be stored in an order database 1918. If this is a new order, a new record may be created for the order. In one embodiment, order database 1918 can be one of several databases operated by cloud infrastructure system 1918 and operated in conjunction with other system elements.

At step 1938, the order information may be forwarded to an order management module 1920 that may be configured to perform billing and accounting functions related to the order, such as verifying the order, and upon verification, booking the order.

At step 1940, information regarding the order may be communicated to an order orchestration module 1922 that is configured to orchestrate the provisioning of services and resources for the order placed by the customer. In some instances, order orchestration module 1922 may use the services of order provisioning module 1924 for the provisioning. In certain embodiments, order orchestration module 1922 enables the management of business processes associated with each order and applies business logic to determine whether an order should proceed to provisioning.

As shown in the embodiment depicted in FIG. 19, at step 1942, upon receiving an order for a new subscription, order orchestration module 1922 sends a request to order provisioning module 1924 to allocate resources and configure resources needed to fulfill the subscription order. Order provisioning module 1924 enables the allocation of resources for the services ordered by the customer. Order provisioning module 1924 provides a level of abstraction between the cloud services provided by cloud infrastructure system 1900 and the physical implementation layer that is used to provision the resources for providing the requested services. This enables order orchestration module 1922 to be isolated from implementation details, such as whether or not services and resources are actually provisioned on the fly or pre-provisioned and only allocated/assigned upon request.

At step 1944, once the services and resources are provisioned, a notification may be sent to the subscribing customers indicating that the requested service is now ready for use. In some instance, information (e.g. a link) may be sent to the customer that enables the customer to start using the requested services.

At step 1946, a customer's subscription order may be managed and tracked by an order management and monitoring module 1926. In some instances, order management and monitoring module 1926 may be configured to collect usage statistics regarding a customer use of subscribed services. For example, statistics may be collected for the amount of storage used, the amount data transferred, the number of users, and the amount of system up time and system down time, and the like.

In certain embodiments, cloud infrastructure system 1900 may include an identity management module 1928 that is configured to provide identity services, such as access management and authorization services in cloud infrastructure system 1900. In some embodiments, identity management module 1928 may control information about customers who wish to utilize the services provided by cloud infrastructure system 1902. Such information can include information that authenticates the identities of such customers and information that describes which actions those customers are authorized to perform relative to various system resources (e.g., files, directories, applications, communication ports, memory segments, etc.). Identity management module 1928 may also include the management of descriptive information about each customer and about how and by whom that descriptive information can be accessed and modified.

FIG. 20 illustrates an exemplary computer system 2000 that may be used to implement an embodiment of the present disclosure. In some embodiments, computer system 2000 may be used to implement any of the various servers and computer systems described above. As shown in FIG. 20, computer system 2000 includes various subsystems including a processing unit 2004 that communicates with a number of peripheral subsystems via a bus subsystem 2002. These peripheral subsystems may include a processing acceleration unit 2006, an I/O subsystem 2008, a storage subsystem 2018 and a communications subsystem 2024. Storage subsystem 2018 may include tangible computer-readable storage media 2022 and a system memory 2010.

Bus subsystem 2002 provides a mechanism for letting the various components and subsystems of computer system 2000 communicate with each other as intended. Although bus subsystem 2002 is shown schematically as a single bus, alternative embodiments of the bus subsystem may utilize multiple buses. Bus subsystem 2002 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. For example, such architectures may include an Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, which can be implemented as a Mezzanine bus manufactured to the IEEE P1386.1 standard, and the like.

Processing subsystem 2004 controls the operation of computer system 2000 and may comprise one or more processing units 2032, 2034, etc. A processing unit may include be one or more processors, including single core or multicore processors, one or more cores of processors, or combinations thereof. In some embodiments, processing subsystem 2004 can include one or more special purpose co-processors such as graphics processors, digital signal processors (DSPs), or the like. In some embodiments, some or all of the processing units of processing subsystem 2004 can be implemented using customized circuits, such as application specific integrated circuits (ASICs), or field programmable gate arrays (FPGAs).

In some embodiments, the processing units in processing subsystem 2004 can execute instructions stored in system memory 2010 or on computer readable storage media 2022. In various embodiments, the processing units can execute a variety of programs or code instructions and can maintain multiple concurrently executing programs or processes. At any given time, some or all of the program code to be executed can be resident in system memory 2010 and/or on computer-readable storage media 2022 including potentially on one or more storage devices. Through suitable programming, processing subsystem 2004 can provide various functionalities described above for semantic searching.

In certain embodiments, a processing acceleration unit 2006 may be provided for performing customized processing or for off-loading some of the processing performed by processing subsystem 2004 so as to accelerate the overall processing performed by computer system 2000.

I/O subsystem 2008 may include devices and mechanisms for inputting information to computer system 2000 and/or for outputting information from or via computer system 2000. In general, use of the term “input device” is intended to include all possible types of devices and mechanisms for inputting information to computer system 2000. User interface input devices may include, for example, a keyboard, pointing devices such as a mouse or trackball, a touchpad or touch screen incorporated into a display, a scroll wheel, a click wheel, a dial, a button, a switch, a keypad, audio input devices with voice command recognition systems, microphones, and other types of input devices. User interface input devices may also include motion sensing and/or gesture recognition devices such as the Microsoft Kinect® motion sensor that enables users to control and interact with an input device, the Microsoft Xbox® 360 game controller, devices that provide an interface for receiving input using gestures and spoken commands. User interface input devices may also include eye gesture recognition devices such as the Google Glass® blink detector that detects eye activity (e.g., “blinking” while taking pictures and/or making a menu selection) from users and transforms the eye gestures as input into an input device (e.g., Google Glass®). Additionally, user interface input devices may include voice recognition sensing devices that enable users to interact with voice recognition systems (e.g., Siri® navigator), through voice commands.

Other examples of user interface input devices include, without limitation, three dimensional (3D) mice, joysticks or pointing sticks, gamepads and graphic tablets, and audio/visual devices such as speakers, digital cameras, digital camcorders, portable media players, webcams, image scanners, fingerprint scanners, barcode reader 3D scanners, 3D printers, laser rangefinders, and eye gaze tracking devices. Additionally, user interface input devices may include, for example, medical imaging input devices such as computed tomography, magnetic resonance imaging, position emission tomography, medical ultrasonography devices. User interface input devices may also include, for example, audio input devices such as MIDI keyboards, digital musical instruments and the like.

User interface output devices may include a display subsystem, indicator lights, or non-visual displays such as audio output devices, etc. The display subsystem may be a cathode ray tube (CRT), a flat-panel device, such as that using a liquid crystal display (LCD) or plasma display, a projection device, a touch screen, and the like. In general, use of the term “output device” is intended to include all possible types of devices and mechanisms for outputting information from computer system 2000 to a user or other computer. For example, user interface output devices may include, without limitation, a variety of display devices that visually convey text, graphics and audio/video information such as monitors, printers, speakers, headphones, automotive navigation systems, plotters, voice output devices, and modems.

Storage subsystem 2018 provides a repository or data store for storing information that is used by computer system 2000. Storage subsystem 2018 provides a tangible non-transitory computer-readable storage medium for storing the basic programming and data constructs that provide the functionality of some embodiments. Software (programs, code modules, instructions) that when executed by processing subsystem 2004 provide the functionality described above may be stored in storage subsystem 2018. The software may be executed by one or more processing units of processing subsystem 2004. Storage subsystem 2018 may also provide a repository for storing data used in accordance with the present disclosure.

Storage subsystem 2018 may include one or more non-transitory memory devices, including volatile and non-volatile memory devices. As shown in FIG. 20, storage subsystem 2018 includes a system memory 2010 and a computer-readable storage media 2022. System memory 2010 may include a number of memories including a volatile main random access memory (RAM) for storage of instructions and data during program execution and a non-volatile read only memory (ROM) or flash memory in which fixed instructions are stored. In some implementations, a basic input/output system (BIOS), containing the basic routines that help to transfer information between elements within computer system 2000, such as during start-up, may typically be stored in the ROM. The RAM typically contains data and/or program modules that are presently being operated and executed by processing subsystem 2004. In some implementations, system memory 2010 may include multiple different types of memory, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

By way of example, and not limitation, as depicted in FIG. 20, system memory 2010 may store application programs 2012, which may include client applications, Web browsers, mid-tier applications, relational database management systems (RDBMS), etc., program data 2014, and an operating system 2016. By way of example, operating system 2016 may include various versions of Microsoft Windows®, Apple Macintosh®, and/or Linux operating systems, a variety of commercially-available UNIX® or UNIX-like operating systems (including without limitation the variety of GNU/Linux operating systems, the Google Chrome® OS, and the like) and/or mobile operating systems such as iOS, Windows® Phone, Android® OS, BlackBerry® 10 OS, and Palm® OS operating systems.

Computer-readable storage media 2022 may store programming and data constructs that provide the functionality of some embodiments. Software (programs, code modules, instructions) that when executed by processing subsystem 2004 a processor provide the functionality described above may be stored in storage subsystem 2018. By way of example, computer-readable storage media 2022 may include non-volatile memory such as a hard disk drive, a magnetic disk drive, an optical disk drive such as a CD ROM, DVD, a Blu-Ray® disk, or other optical media. Computer-readable storage media 2022 may include, but is not limited to, Zip® drives, flash memory cards, universal serial bus (USB) flash drives, secure digital (SD) cards, DVD disks, digital video tape, and the like. Computer-readable storage media 2022 may also include, solid-state drives (SSD) based on non-volatile memory such as flash-memory based SSDs, enterprise flash drives, solid state ROM, and the like, SSDs based on volatile memory such as solid state RAM, dynamic RAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, and hybrid SSDs that use a combination of DRAM and flash memory based SSDs. Computer-readable media 2022 may provide storage of computer-readable instructions, data structures, program modules, and other data for computer system 2000.

In certain embodiments, storage subsystem 2000 may also include a computer-readable storage media reader 2020 that can further be connected to computer-readable storage media 2022. Together and, optionally, in combination with system memory 2010, computer-readable storage media 2022 may comprehensively represent remote, local, fixed, and/or removable storage devices plus storage media for storing computer-readable information.

In certain embodiments, computer system 2000 may provide support for executing one or more virtual machines. Computer system 2000 may execute a program such as a hypervisor for facilitating the configuring and managing of the virtual machines. Each virtual machine may be allocated memory, compute (e.g., processors, cores), I/O, and networking resources. Each virtual machine typically runs its own operating system, which may be the same as or different from the operating systems executed by other virtual machines executed by computer system 2000. Accordingly, multiple operating systems may potentially be run concurrently by computer system 2000. Each virtual machine generally runs independently of the other virtual machines.

Communications subsystem 2024 provides an interface to other computer systems and networks. Communications subsystem 2024 serves as an interface for receiving data from and transmitting data to other systems from computer system 2000. For example, communications subsystem 2024 may enable computer system 2000 to establish a communication channel to one or more client computing devices via the Internet for receiving and sending information from and to the client computing devices.

Communication subsystem 2024 may support both wired and/or wireless communication protocols. For example, in certain embodiments, communications subsystem 2024 may include radio frequency (RF) transceiver components for accessing wireless voice and/or data networks (e.g., using cellular telephone technology, advanced data network technology, such as 3G, 4G or EDGE (enhanced data rates for global evolution), WiFi (IEEE 802.11 family standards, or other mobile communication technologies, or any combination thereof), global positioning system (GPS) receiver components, and/or other components. In some embodiments communications subsystem 2024 can provide wired network connectivity (e.g., Ethernet) in addition to or instead of a wireless interface.

Communication subsystem 2024 can receive and transmit data in various forms. For example, in some embodiments, communications subsystem 2024 may receive input communication in the form of structured and/or unstructured data feeds 2026, event streams 2028, event updates 2030, and the like. For example, communications subsystem 2024 may be configured to receive (or send) data feeds 2026 in real-time from users of social media networks and/or other communication services such as Twitter® feeds, Facebook® updates, web feeds such as Rich Site Summary (RSS) feeds, and/or real-time updates from one or more third party information sources.

In certain embodiments, communications subsystem 2024 may be configured to receive data in the form of continuous data streams, which may include event streams 2028 of real-time events and/or event updates 2030, that may be continuous or unbounded in nature with no explicit end. Examples of applications that generate continuous data may include, for example, sensor data applications, financial tickers, network performance measuring tools (e.g. network monitoring and traffic management applications), clickstream analysis tools, automobile traffic monitoring, and the like.

Communications subsystem 2024 may also be configured to output the structured and/or unstructured data feeds 2026, event streams 2028, event updates 2030, and the like to one or more databases that may be in communication with one or more streaming data source computers coupled to computer system 2000.

Computer system 2000 can be one of various types, including a handheld portable device (e.g., an iPhone® cellular phone, an iPad® computing tablet, a PDA), a wearable device (e.g., a Google Glass® head mounted display), a personal computer, a workstation, a mainframe, a kiosk, a server rack, or any other data processing system.

Due to the ever-changing nature of computers and networks, the description of computer system 2000 depicted in FIG. 20 is intended only as a specific example. Many other configurations having more or fewer components than the system depicted in FIG. 20 are possible. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will appreciate other ways and/or methods to implement the various embodiments.

Although specific embodiments of the present disclosure have been described, various modifications, alterations, alternative constructions, and equivalents are also encompassed within the scope of the present disclosure. The modifications include any relevant combination of the disclosed features. Embodiments of the present disclosure are not restricted to operation within certain specific data processing environments, but are free to operate within a plurality of data processing environments. Additionally, although embodiments of the present disclosure have been described using a particular series of transactions and steps, it should be apparent to those skilled in the art that the scope of the present disclosure is not limited to the described series of transactions and steps. Various features and aspects of the above-described embodiments may be used individually or jointly.

Further, while embodiments of the present disclosure have been described using a particular combination of hardware and software, it should be recognized that other combinations of hardware and software are also within the scope of the present disclosure. Embodiments of the present disclosure may be implemented only in hardware, or only in software, or using combinations thereof. The various processes described herein can be implemented on the same processor or different processors in any combination. Accordingly, where components or modules are described as being configured to perform certain operations, such configuration can be accomplished, e.g., by designing electronic circuits to perform the operation, by programming programmable electronic circuits (such as microprocessors) to perform the operation, or any combination thereof. Processes can communicate using a variety of techniques including but not limited to conventional techniques for interprocess communication, and different pairs of processes may use different techniques, or the same pair of processes may use different techniques at different times.

The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. It will, however, be evident that additions, subtractions, deletions, and other modifications and changes may be made thereunto without departing from the broader spirit and scope as set forth in the claims. Thus, although specific embodiments have been described, these are not intended to be limiting. Various modifications and equivalents are within the scope of the following claims. 

What is claimed is:
 1. A method implemented by a computer system of a data analytic system, comprising: processing an input string as a search query for data from one or more data sources, wherein the input string is processed to identify a plurality of terms; performing a first comparison of the plurality of terms to a plurality of subject areas in a subject area index, wherein each subject area of the plurality of subject areas is associated with a respective set of terms, and wherein performing the first comparison includes comparing the plurality of terms to the respective set of terms; selecting, based on a first result of first comparison, a set of subject areas in the plurality of subject areas, wherein the set of subject areas is selected as having a respective set of terms that is a closest match with the plurality of terms, wherein each of the set of subject areas is associated with a set of columns, and wherein each of the set of columns is associated with an attribute and one or more terms in a respective subject area; determining a subject area column index, the subject area column index including a respective set of columns from each of the set of subject areas; performing a second comparison of the plurality of terms to the subject area column index, wherein, for each column in the subject area column index, the plurality of terms is compared to the attribute and the one or more terms in the respective subject area, each associated with the column; selecting, based on a second result of the second comparison, one or more terms of the plurality of terms, wherein the one or more terms are selected based on the one or more terms matching a threshold number of a particular one or more terms in a particular respective subject area, wherein each of the particular one or more terms in the particular respective subject area are associated with values; determining one or more matching terms from the one or more terms selected from the plurality of terms by comparing the one or more terms selected from the plurality of terms to the values associated with the particular one or more terms in the particular respective subject area; generating a query based on the one or more matching terms; outputting the query to a second computer server for the second computer server to place a request for data for the query to be retrieved; obtaining the data for the query; and outputting the data for the query for display on a client system.
 2. The method of claim 1, wherein processing the input string to identify the plurality of terms includes: tokenizing the input string to identify a set of terms based on one or more characters; determining a stem word for each of the set of terms; determining a relationship between any pair of terms in the set of terms; and generating the plurality of terms based on the set of terms and the relationship between any terms in the set of terms.
 3. The method of claim 1, further comprising: generating one or more first data structures for the subject area column index.
 4. The method of claim 3, wherein the subject area column index has a different plurality of columns for each of the set of subject areas.
 5. The method of claim 4, further comprising: generating one or more second data structures, the one or more second data structures including values associated with one or more terms in a respective subject area, wherein the one or more matching terms are determined using the one or more second data structures.
 6. The method of claim 1, wherein the one or more matching terms includes a set of terms, wherein generating the query includes determining an order in the set of terms based on comparing of the one or more terms selected from the plurality of terms to the values associated with the particular one or more terms in the particular respective subject area.
 7. The method of claim 1, further comprising: performing a crawl process based on a crawl configuration; and generating one or more data structures as the subject area index based on the crawl process.
 8. The method of claim 1, wherein obtaining the data for the query includes retrieving the data from one or more data sources based on the query, wherein the query is different from the search query, and wherein the query has a format of a logical structured query language (SQL).
 9. The method of claim 8, further comprising: identifying one or more dimensions for the one or more matching terms; using the one or more dimensions to determine a layout for presenting the retrieved data; and generating a graphical interface based on the layout.
 10. A system comprising: one or more processors; and a memory accessible to the one or more processors, the memory comprising one or more instructions that, upon execution by the one or more processors, causes the one or more processors to: process an input string as a search query for data from one or more data sources, wherein the input string is processed to identify a plurality of terms; perform a first comparison of the plurality of terms to a plurality of subject areas in a subject area index, wherein each subject area of the plurality of subject areas is associated with a respective set of terms, and wherein performing the first comparison includes comparing the plurality of terms to the respective set of terms; selecting, based on a first result of the first comparison, a set of subject areas in the plurality of subject areas, wherein the set of subject areas is selected as having respective set of terms that is a closest match with the plurality of terms, wherein each of the set of subject areas is associated with a set of columns, and wherein each of the set of columns is associated with an attribute and one or more terms in a respective subject area; determining a subject area column index, the subject area column index including a respective set of columns from each of the set of subject areas; perform a second comparison of the plurality of terms to the subject area column index, wherein, for each column in the subject area column index, the plurality of terms is compared to the attribute and the one or more terms in the respective subject area, each associated with the column; selecting, based on a second result of the second comparison, one or more terms of the plurality of terms, wherein the one or more terms are selected based on the one or more terms matching a threshold number of a particular one or more terms in a particular respective subject area, wherein each of the particular one or more terms in the particular respective subject area are associated with values; determine one or more matching terms from the one or more terms selected from the plurality of terms to the values associated with the particular one or more terms in the particular respective subject area; generate a query based on the one or more matching terms; output the query to a second computer server for the second computer server to place a request for data for the query to be retrieved; obtain the data for the query; and output the data for the query for display on a client system.
 11. The system of claim 10, wherein processing the input string to identify the plurality of terms includes: tokenizing the input string to identify a set of terms based on one or more characters; determining a stem word for each of the set of terms; determining a relationship between any pair of terms in the set of terms; and generating the plurality of terms based on the set of terms and the relationship between any terms in the set of terms.
 12. The system of claim 10, wherein the one or more instructions, upon execution by the one or more processors, further causes the one or more processors to: generate one or more first data structures for the subject area column index.
 13. The system of claim 12, wherein the subject area column index has a different plurality of columns for each of the set of subject areas.
 14. The system of claim 13, wherein the one or more instructions, upon execution by the one or more processors, further causes the one or more processors to: generate one or more second data structures, the one or more second data structures including values associated with one or more terms in a respective subject area, wherein the one or more matching terms are determined using the one or more second data structures.
 15. The system of claim 10, wherein the query is different from the search query, and wherein the query has a format of a logical structured query language (SQL).
 16. The system of claim 10, wherein the one or more matching terms includes a set of terms, wherein generating the query includes determining an order in the set of terms based on comparing of the one or more terms selected from the plurality of terms to the values associated with the particular one or more terms in the particular respective subject area.
 17. A non-transitory computer readable medium storing one or more instructions that, upon execution by one or more processors, causes the one or more processors to: process an input string as a search query for data from one or more data sources, wherein the input string is processed to identify a plurality of terms; perform a first comparison of the plurality of terms to a plurality of subject areas in a subject area index, wherein each subject area of the plurality of subject areas is associated with a respective set of terms, and wherein performing the first comparison includes comparing the plurality of terms to the respective set of terms; selecting, based on a first result of the first comparison, a set of subject areas in the plurality of subject areas, wherein the set of subject areas is selected as having respective set of terms that is a closest match with the plurality of terms, wherein each of the set of subject areas is associated with a set of columns, and wherein each of the set of columns is associated with an attribute and one or more terms in a respective subject area; determining a subject area column index, the subject area column index including a respective set of columns from each of the set of subject areas; perform a second comparison of the plurality of terms to the subject area column index, wherein, for each column in the subject area column index, the plurality of terms is compared to the attribute and the one or more terms in the respective subject area, each associated with the column; selecting, based on a second result of the second comparison, one or more terms of the plurality of terms, wherein the one or more terms are selected based on the one or more terms matching a threshold number of a particular one or more terms in a particular respective subject area, wherein each of the particular one or more terms in the particular respective subject area are associated with values; determine one or more matching terms from the one or more terms selected from the plurality of terms to the values associated with the particular one or more terms in the particular respective subject area; generate a query based on the one or more matching terms; output the query to a second computer server for the second computer server to place a request for data for the query to be retrieved; obtain the data for the query; and output the data for the query for display on a client system.
 18. The non-transitory computer readable medium of claim 17, wherein the one or more instructions, upon execution by the one or more processors, further causes the one or more processors to: generate one or more first data structures for the subject area column index, wherein the subject area column index has a different plurality of columns for each of the set of subject areas; and generate one or more second data structures, the one or more second data structures including values associated with one or more terms in a respective subject area, wherein the one or more matching terms are determined using the one or more second data structures.
 19. The non-transitory computer readable medium of claim 17, wherein obtaining the data for the query includes retrieving the data from one or more data sources based on the query, wherein the query is different from the search query, and wherein the query has a format of a logical structured query language (SQL).
 20. The non-transitory computer readable medium of claim 19, wherein the one or more instructions, upon execution by the one or more processors, further causes the one or more processors to: identify one or more dimensions for the one or more matching terms; using the one or more dimensions to determine a layout for presenting the retrieved data; and generate a graphical interface based on the layout. 